Professionals Examinations

Professional Examinations + Language Exams Content

INformation Systems Core

1.0 Information Systems   (20%) (D3)
Organizational Role, Business Drivers, Strategy, Structure, Acquisition & Management
1.1 The Role of IS in the Organization
1.2 Business Drivers and Information Systems Valuation
1.3 Structuring the IS Organization
1.4 Acquiring Information Technology Resources and Capabilities (In-house Development, External Acquisition, or Outsourcing)
1.5 Information Systems Technology Components and System Types
2.0 Data and Information Management (15%) D3
This section covers the core concepts in data and information management using conceptual data modeling techniques. This section also includes coverage of basic database administration tasks and key concepts of data quality and data security.
2.1 Data Management
2.2 Data Governance
2.3 Data Modeling
2.4 Meta-data Management
2.5 Data Quality Management
2.6 Data Security Management
2.7 Database Approach
3.0 Enterprise Architecture (15%)    D3
This section covers the design, selection, implementation and management of enterprise IT solutions. The focus is on frameworks and strategies for infrastructure management, system administration, data/information architecture, content management, distributed computing, middleware, legacy system integration, system consolidation, software selection, total cost of ownership calculation, IT investment analysis, and emerging technologies.
3.1 Enterprise Architecture Types and Structures
3.2 Enterprise Architecture Frameworks, Methodologies, and Measurements
4.0 IT Infrastructure (15%)    D3
This section covers topics related to both computer and systems architecture and communication networks, with an overall focus on the services and capabilities that IT infrastructure solutions enable in an organizational context.
4.1 Systems Concepts
4.2 Operating Systems
4.3 Networking
4.5 IT Control and Service Management Frameworks (COBIT, ITIL, etc.)
4.6 Managing IT Infrastructure and Business continuity
5.0 IS Project Management (15%)    D3
Project management in the modern organization is a complex team-based activity, where various types of technologies (including project management software as well as software to support group collaboration) are an inherent part of the project management process. This section focuses on a systematic methodology for initiating, planning, executing, controlling, and closing projects.
5.1 Introduction to Project Management
5.2 The Project Management Lifecycle
5.3 Managing Project Teams
5.4 Project Initiation and Planning
5.5 Managing Project Scheduling
5.6 Managing Project Resources
5.7 Managing Project Quality and Risk
5.8 Systems Procurement
5.9 Project Execution, Control and Closure
6.0 Systems Analysis & Design (15%)    D3
This section focuses on a systematic methodology for analyzing a business problem or opportunity, determining what role, if any, computer-based technologies can play in addressing the business need, articulating business requirements for the technology solution, specifying alternative approaches to acquiring the technology capabilities needed to address the business requirements, and specifying the requirements for an information systems solution.
6.1 Business Process Management
6.2 Structuring of IT-based Opportunities into Projects
6.3 Analysis and Specification of System Requirements
6.4 Implementation Strategies

Big DATA

1. Big Data Terminology

a. Volume, Variety, Velocity, Veracity
b. Multi-structured data
c. Data Ingestion

2. Big Data Concepts

a. Discovery, Search, Navigation
b. Land Data (Access, Collect), Manage, Store
c. Multi-source integration
d. Big Data Analytics
e. Structured controlled data
f. Analyzing unstructured data
g. Real time (Streaming data analytics)
h. Full Contextual Analysis
i. Textual Disambiguation
j. Big Data Integration

3. Technical Considerations

a. Storage volume and relationships
b. Data Architecture
c. Data Ingestion Requirements
d. Infrastructure considerations
e. Feature extraction and metadata
f. Consistency/Redundancy
g. Implementation considerations

4. Big Data Governance - sources, data quality, integration, security, life cycle, master data
management & definitions

a. Organizational Structures and Awareness
b. Stewardship
c. Policy
d. Value Creation
e. Fit of Data
f. Data Risk Management & Compliance
g. Information Security & Privacy
h. Data Quality management
i. Classification and metadata
j. Information lifecycle management
k. Audit information, logging and reporting

Business Intelligence and Data Analytics

1.0 Business Intelligence Concepts & Roles (10)
     1.1 Definitions and Business Drivers (5)
     1.2 Business Intelligence in Organizational Roles (5)
2.0 Business Management Perspectives (15)
     2.1 Business concepts principles and guidelines (4)
     2.2 Performance Management (5)
     2.3 Ongoing monitoring and controlling execution (6)
3.0 Analytics Techniques and Usage (65)
     3.1 Modeling (10)
     3.2 Business and Data Analysis (14)
     3.3 Data Visualization Techniques (4)
     3.4 Statistics (14)
     3.5 Assessments (4)
     3.6 Measurements and monitoring (5)
     3.7 Decision making (14)
4.0 Business Intelligence / Decision Support Systems (10)
     4.1 Front end business intelligence technologies (6)
     4.2 Back end tools, applications and technologies (4)

Business and technology Management

1. Business Systems (15%), D3
1.1 Business Functions 1.2 Organizational Performance (Assets Management)
1.3 Enterprise Architecture
2. Management Systems (10%), D3
2.1 Strategic, Tactical and Operational Systems
2.2 Planning, Organizing, Controlling and Coordination
2.3 Optimization: Storage, Production, Distribution, Services & Resources Management
2.4 Quality and Process Management
2.5 Organizational Trasformation & Change Management
3. Planning (15%), D3
3.1 Measuring and Management
3.2 Business Process Management and Continuous Improvement
3.3 Analytics (Quantitative and Qualitative)
3.4 Research and Development
4. Administration and Decision Making (10%), D3
4.1 Decisions Support
4.2 Management Roles & Responsibilities
5. Control and Coordination (20%), D3
5.1 Programs
5.2 Projects
5.3 Scope
5.4 People
5.5 Fixed Assets and Equipment
5.6 Budgeting, Cash Flow, Cost, Investment & ROI
5.7 Risk
5.8 Time
6. Business Information Systems and Technology (30%), D3
6.1 Business Data (Organizing, Access, Storage, Protection, Archiving, Big Data)
6.2 Business Processes
6.3 Systems Architectures
6.4 Technologies

Business Information Systems Exam

The Business Information Systems exam is intended to help define the body of knowledge and professional practices associated with the development and management of Business Information Systems. The BIS exam is designed to test the candidate's \knowledge of the usage of Information Systems theory and practice at a level of competency appropriate to senior IS professionals.
Exam Outline
1. Business Information Systems Applications
1.1 Financial Planning/Decision Support
1.2 Accounting
1.3 Organizational Performance
1.4 Marketing and Sales
1.5 Materials Management
1.6 Production and Distribution Management

2. The Business Information Systems Environment
2.1 System Analysis/Design Function
2.2 Data Base Design Function
2.3 Application Programming Function
2.4 Computer Operations Function
2.5 Systems Programming Function
2.6 Quality Control Function
2.7 Information Center Function

3. Business Information System Considerations
3.1 User/IS Relations
3.2 Business Economics
3.3 IS Resource Management
3.4 EDP Equipment Use
3.5 Software Development Environment

Business Process Management Exam

1.0 Business Process Management Concepts & Roles (15) Section 1 Total
1.1 Definitions (6) D3
1.2 BPM Organizational Roles & Responsibilities (9) D3
2.0 Business Management Perspectives (20) Section 2 Total
2.1 Business Concepts, Principles and Guidelines (6) D3
2.2 Performance Management (7) D4
2.3 Ongoing Monitoring and Controlling Execution (7) D4
3.0 BPM Methodology Approaches and Techniques (40) Section 2 Total
3.1 Enterprise Process Planning (6) D4
3.2 Process Analysis and Design (24) D4
3.3 Process Management Improvement (10) D4
4.0 Business Process Management Technology (25) Section 4 Total
4.1 Business Process Management Systems (BPMS) Implementation (10) D3
4.2 BPMS Technology Types (15) D4

Cyber Security Exam

1. Foundations of Cyber Security (10%)
1. 1. The importance of information security in business continuity.
2. 2. Security threats and managerial controls to counter threats
3. 3. Procedures for ensuring compliance with security policies, standards, plans for security implementation
4. 4. Legal implications of security standards and implementation
5. 5. Sourcing, managing and implementing an effective security program
6. 6. Audit, recovery and coping with security breaches
2. Computer Hacking Forensic Investigators (24%)
1. 1. Function and limitations of forensic investigations.
2. 2. Procedures used in conducting forensic investigations.
3. 3. Legal issues of preparing for and performing digital forensic analysis.
4. 4. Digital evidence – storage, preparation and requirements.
5. 5. Digital forensic lab and digital forensics tools
3. Ethical Hacking (11%)
1. 1. What constitutes ethical hacking
2. 2. Footprinting and reconnaissance
3. 3. Scanning networks, penetration testing
4. 4. Enumeration
5. 5. Hacking
  1. 1. system (OS, wired and wireless),
  2. 2. session
  3. 3. webserver
  4. 4. web application
  5. 5. SQL insertions
  6. 6. Buffer overflow
  7. 7. Encryption – Decryption
6. 6. Trojans, backdoors, viruses, worms, sniffers
7. 7. Social engineering, denial of service
8. 8. Evading IDS, Firewalls, and Honeypots
4. Incident response handling and disaster recovery (14%)
1. 1. Information security risk and risk management
2. 2. Planning for organizational readiness
3. 3. Contingency strategies
4. 4. Incident response and recovery
  1. 1. Detection & decision making
  2. 2. Organizing and preparing for CSIRT
  3. 3. Response strategies
  4. 4. Recovery and maintenance
5. 5. Disaster Recovery:
  1. 1. Preparation and implementation
  2. 2. Operation and maintenance
6. 6. Business continuity planning
7. 7. Crises management and international standards
5. Network Security Administration (17%)
1. 1. Networks and Security issues
2. 2. Malware and social engineering attacks
3. 3. Data, Application and Network based attacks
4. 4. Cryptography and de-encryption
5. 5. Mobile, Wireless and Server based attacks
6. 6. Authentication, Accounts and vulnerability assessments
7. 7. Business continuity and risk mitigation
8. 8. Legal and ethical factors of courses of action and recovery
6. Secure Programming issues (14%)
1. 1. Authentication, Authorization, Database Design and Validation, Encryption, Exception Handling & Logging, Framework Security, Session Management, common web and application attacks
  1. 1. .NET, C#, MS-SQL
  2. 2. Java, SQL, other databases
7. Security Analyst(10%)
1. 1. Range of defenses against attacks
2. 2. Legal, ethical, privacy issues related to computer and information security
3. 3. Improving security over time, responses to security crime and developing scenarios to test possible outcomes.
4. 4. Importance, creation & maintenance of a Disaster Recovery in the enterprise
5. 5. Developing a secure network
6. 6. Security policy and procedures frameworks and implementation
7. 7. Cloud and other virtualization technologies
8. 8. Use and role of traditional and virtual technologies in disaster recovery

Data Foundations Examination

1.   Data, Information & Knowledge (3%) D2
2.   Describing, Understanding & Managing Data    (35%)        D4
3.   Data Roles: (4%) D2
4.   Data Models and Relationships (18%)      D3
5.   Data Value and Quality (4%) D3
6.   Data Access, Storage, Protection & Security     (6%) D4
7.   Data Atrophy, Renewal and Removal, Distribution (2%) D3
8.   Data Uses (Historical, legal...) (6%) D2
9.   Data Storage Technologies and purposes:       (4%) D2
10.   Evolving Business and Data Analytics (Tools)(12%) D3
11.   Corporate mergers, data integration (5%) D2
12.   Data Strategy & Planning (4%) D2
13.   Document and Record Management (2%) D3

DATA AND INFORMATION QUALITY

1.0 Data & Information Quality Concepts, Process & Roles 20%
    1.1 Concepts and Business Drivers (14)
    1.2 Data & Information Quality Organizational Roles (6)
2.0 Data & Information Quality Audit Process 30%
    2.1 Data & Information Quality Assessment (15)
    2.2 Data Profiling Analysis (15)
3.0. Data & Information Quality Remediation 30%
    3.1 Cleanup Methods (15)
    3.2 Process Improvement (15)
4.0 Ongoing Data & Information Quality Activities 20%
    4.1 Data Certification Process (15)
    4.2 Data Governance Framework (5)

DATA COMMUNICATIONS AND INTERNETWORKING EXAM

1. Data Communications Concepts (12) D4
    1.1 Concepts
    1.2 Protocols
    1.3 Layering
    1.4 Interfaces
    1.5 Wireless, LANs, WANs, MANs
    1.6 Internet Protocol (IP) Telephony
2. Networking Concepts (15) D5
    2.1 Topology
    2.2 Connectivity
    2.3 Queuing theory
    2.4 Flow and capacity
3. The ISO Open Systems Interconnect (OSI) Reference Model (13) D4
    3.1 Physical layer
    3.2 Data link layer
    3.3 Network Layer
    3.4 Transport Layer
    3.5 Session Layer
    3.6 Presentation Layer
    3.7 Application Layer
4. TCP/IP or Internet Reference Model (20) D5
    4.1 Physical Layer
    4.2 Data Link Layer
    4.3 Network / Internet Layer
    4.4 Transport Layer
    4.5 Application Layer
5. Established Communications Systems (15) D5
    5.1 Standards organizations and standards
    5.2 Telecommunications
    5.3 Data Communications
    5.4 Computer Communications and Networks
    5.5 Wireless Technologies
6. Hardware (15) D4
    6.1 Data Switches
    6.2 Modems/Codecs
    6.3 Multiplexors/Concentrators
    6.4 Communications controllers
    6.5 Front-end processors
    6.6 Buses and channels
    6.7 Fiber optical devices
    6.8 Connectors and cables
    6.9 Telephone systems
    6.10 Computer workstations
    6.11 Installation of equipment
    6.12 Diagnostic equipment
    6.13 Wireless equipment
    6.14 Broadband and baseband

Data Modeling (Data Development)

1.0 Data Development Concepts and Technology Types (17) Section 1 Total
    1.1 Concepts (12) D3
    1.2 Tools and Technology Types (5) D3
2.0 Data Analysis and Design (60) Section 2 Total
    2.1 Analyzing Information Requirements (3) D4
    2.2 Data Model / Design Components (57) D4
3.0 Related Data Designs (7) Section 3 Total
    3.1 Information Product Designs (2) D4
    3.2 Data Access and Data Integration Services Designs (5) D4
4.0 Data Model and Design Quality Management (10) Section 3 Total
    4.1 Data Model Design Standards, Versioning and Integration (7) D3
    4.2 Data Model / Database Design Reviews (DBA3.6) (3) D3
5.0 Data Implementation (6) Section 4 Total
    5.1 Database Development and Testing (4) D4
    5.1 Database Deployment (2) D4

DATA Governance & Stewardship

1.0  Data Governance Concepts, Business Drivers and Roles (10)
       1.1 Concepts and Definitions
       1.2 Business Drivers and Organizational Awareness
2.0  Data Governance and Stewardship Roles (20)
       2.1 Data Governance Roles and Organizations
       2.2 Data Stewardship Roles
3.0  Data Governance Inputs and Deliverables (20)
       3.1  Inputs
       3.2  Primary Deliverables
4.0  Data Governance Function (20)
       4.1 Data Governance Capabilities Assessment
       4.2 Data Governance Function Activities
4.3 Data Governance Ethics
5.0  Fundamental Knowledge Areas for Data Stewardship Activities (30)
       5.1 Enterprise Data Architecture Management
       5.2 Data Development
       5.3 Data Operations Management
       5.4 Data Security Management
       5.5 Meta-data Management
       5.6 Reference and Master Data Management
       5.7 Data Warehousing and Business Intelligence
       5.8 Document and Content Management
       5.9 Data Quality Management

DATA Integration and Interoperability

1. Data Integration and Interoperability Approaches
    1.1 Data Integration and Interoperability Concepts
    1.2 Data Integration Drivers and Value
2. DII Architectures and Data Patterns
    2.1 DII Architectures
    2.2 DII Data Patterns
3. DII Processes, Technologies, and Standards
    3.1 Techniques and Technologies
    3.2 DII Standards
4. Data Integration Systems
    4.1 System Types
    4.2 Data Integration Systems Development Life Cycle (SDLC)
5. Organizational Roles and DII Governance
    5.1 Organizational DII Roles and Practices
    5.2 DII Governance

DATA Management

1.0. Data Management Process (5)
    1.1 Concepts
    1.2 Data Roles
2.0 Data Governance Function (8)
    2.1 Data Management Planning
    2.2 Data Management Control
3.0 Data Architecture Management Function (10)
    3.1 Architecture Overview
    3.2 Enterprise Architecture Types & Techniques
4.0 Data Development Function (11)
    4.1 Analyzing Data / Information Requirements
    4.2 Data Model Component Management and Data Implementation
5.0 Data Operations Management Function (8)
    5.1 Database Support
    5.2 Data Technology Management
6.0 Data Security Management Function (9)
    6.1 Data Security Principles
    6.2 Data Security Implementation
7.0 Reference and Master Data Management Function (8)
    7.1 Reference Data
    7.2 Master Data Management
8.0 Data Warehousing and Business Intelligence Management Function (12)
    8.1 Data Warehousing
    8.2 Business Intelligence
9.0 Document and Content Management Function (5)
    9.1 Document / Records Management
    9.2 Content Management
10.0 Metadata Management Function (4)
    10.1 Metadata Concepts
    10.2 Metadata Management Implementation
11.0 Data Quality Management Function (14)
    11.1 Data & Information Quality Principles
    11.2 Data & Information Quality Profiling / Assessment / Audit
    11.3 Data & Information Quality Improvement
12.0 Big Data (2)
13.0 Data Integration (3)
14.0 Data Ethics (1)

DATA Base Administration (Data Operations)

1.0. Data Operations Management (56) Section 1 Total
1.0. Data Operations Management (56) Section 1 Total
    1.1 Database Support (50) D4
    1.2 Database Standards (6) D5
2.0. SQL Considerations (14) Section 5 Total
    2.1 SQL Basics (2) D4
    2.2. DDL - Data Definition Language (2) D4
    2.3. DML - Data Manipulation Language (6) D5
    2.4. DCL - Data Control Language (2) D5
    2.5. Data Dictionary (Systables) (2) D5
3.0 Data Technology Management (15) Section 3 Total
    3.1 Planning, Evaluation and Selection (9) D3
    3.1 Implementing Data Technology (6) D3
4.0 Data Security Management (15) Section 3 Total
    4.1 Data Security Principles (8) D3
    4.2 Data Security Implementation (7) D31.1 Database Support (50) D4

DATA science

The Data Science specialty examination involves, collecting and cleansing of data and building new analysis of that data, often simultaneously, to exploit new information about that data and repeating steps after initial understanding and learnings emerge.

1. Business & Technology Issues: Starting with the Question first -What problem are you trying to solve? (16%)
1.1. Business
1.1.1. Data governance
1.1.2. Data security
1.1.3. Data strategy
1.1.4. Change management
1.1.5. Trends and directions in the industry
1.2. Technical/Scientific
2. Data Storage, Big Data and Sources (25%)
2.1. Geo-position data
2.2. Devices data: Internet of Things and Sensor Data
2.3. Social media data:
2.4. Primary data and Published research
2.5. Health research and data,
2.6. Open public data
2.7. Technologies: Hadoop, RDBMS, NoSQL
2.8. Cloud and agile
2.9. Structured and unstructured data
2.10. Databases, Data Marts, Data Lakes
3. Mathematics and Statistical Data Science (13%)
Mathematics and Statistics focuses on the science of learning from data.
3.1. Uncertainty and the role of statistics
3.2. Algorithms and their development
3.3. Data Science Programming Languages
3.3.1. R language
3.3.2. Python
3.3.3. Others – Java, Perl, SAS, SPSS, SQL
3.4. Data science uses
3.5. Data Science techniques (small sample provided)
3.5.1. Regression
3.5.2. Estimation, Intervals
3.5.3. Hypothesis testing
3.5.4. Pattern recognition, supervised learning
3.5.5. Clustering, Segmentation
3.5.6. Time Series, Decision Trees, Random numbers, Monte Carlo
3.5.7. Bayesian statistics; naïve Bayes
3.5.8. Association rules
3.5.9. Nearest neighbour
3.5.10. Model fitting
3.5.11. Predictive modeling
4. Programming Skills (3%)
4.1. Computer programming with R, including JSON
4.2. Substantive Expertise/ Experience
5. The Data Analytic Question and Types of Data & Reporting (4%)
5.1. Summary data
5.2. Descriptive data
5.3. Exploratory data
5.4. How Data are affected
5.4.1. Causal
5.4.2. Mechanistic: Deterministic
5.4.3. Inferential
5.4.4. Predictive
5.4.5. Common mistakes
5.4.5.1. Correlation versus causation
5.4.5.2. Model Building and Testing
5.4.5.3. Small sample size (n) inferences
5.4.5.4. Data dredging
6. Tidying the data – Data cleaning and quality (6%)
6.1. Components of a data set
6.1.1. Raw data
6.1.2. A tidy data set
6.1.3. Metadata
6.1.4. Documented process
6.1.5. Published research
6.2. Common mistakes
6.3. Checking the data
6.3.1. Quirks and potential errors
6.3.2. Coding variables
6.3.2.1. Methods
6.3.2.2. Errors
6.3.2.3. Common mistakes
7. Exploratory analysis (6%)
7.1. Summarizing and visualizing data prior to analysis
7.2. Interactive analysis
7.3. Common Mistakes
8. Statistical Modeling and Inference (7%)
8.1. Best estimate
8.2. Level of uncertainty
8.3. Size
8.4. Exploratory and confirmatory analysis
8.5. Defining population, sample, individuals and data
8.6. Unrepresentative sample, confounders, distribution of missing data, outliers,
8.7. Small or very large samples
8.8. Multiple hypothesis tests and correcting for multiple tests
8.9. Smoothing data over space and time
8.9.1. Regression
8.9.2. Locally weighted scatterplot smoothing
8.9.3. Smoothing splines, moving averages, Loess
8.10. Real sample size
8.11. Common mistakes
8.11.1. Dependencies
8.11.2. p-values versus confidence intervals
8.11.3. Inference with exploration
8.11.4. Assumptions about models and fit
8.11.5. Conclusions about populations
8.11.6. Uncertainty
9. Prediction and Machine Learning (8%)
Creation of training sets from sample data and some variables become features, others become outcomes with the goal to build an algorithm or prediction function taking a new set of data from an individual data set and best guess (estimate) the outcome value.
9.1. Splitting data into training and validation sets
9.2. More data versus better algorithms
9.3. Features versus algorithm
9.4. Definition of error and measure
9.5. Overfitting and validation
9.6. Prediction accuracy and multiple models
9.7. Prediction trade-offs
10. Causality (identifying average affects between noisy variables) (2%)
10.1. Causal data and non-randomized experiments
10.2. Difficulties associated with interpreting cause
10.3. Confirming randomization worked
10.4. Avoiding causal language or techniques
10.5. Common mistake(s)
11. Written Analysis (9%)
11.1. Communicating the message
11.1.1. Question
11.1.2. Writing for non-technical audiences
11.1.3. Text, figures, equations, code (algorithms) and how they contribute to or detract from the story
11.2. Components
11.2.1. Title, Introduction or motivation for the study, including question
11.2.2. Experimental design – source of data, collection methods, relevant technologies and methods used to collect the data.
11.2.3. Data set, tidy data (clean data), sample sizes, number of variables, averages, variances and standard deviations for each variable
11.2.4. Description of the statistics or machine learning models used
I. Precise mathematical model(s)
II. Specification of terms
11.2.5. Results including measures of uncertainty
I. Assumptions
II. Errors
III. Independent or dependent and/or common variance
IV. Sampling and use of different dispersions (heteroskedastic)
11.2.6. Parameters of interest
11.2.6.1. Estimates, scale of interest; Estimate: Why, how and meaning
11.2.6.2. Measures of uncertainty: Standard deviations, confidence intervals, credible intervals
11.2.7. Conclusions and potential problems including with the study
11.2.7.1. Problems: outliers, missing data
11.2.7.2. Exclusions: exploratory analysis, test development – do they contribute to the story or not?
11.2.8. Data Visualizations
11.2.8.1. Graphs, charts, figures, histograms
11.2.8.2. Documentation and Clarification: Colour, Size, Labeling (units of measure), Legends
11.2.8.3. Visualization errors: displaying data badly
11.2.9. References
12. Reproducibility (1%)
12.1. Reproduction of results by third parties for verification
12.2. Data, Figures, R Code, Text
12.2.1. Data – raw, processed, set seed (bootstrap or permutations)
12.2.2. Figures – exploratory, final
12.2.3. R Code – raw, unused scripts; data processing scripts; analysis scripts
12.2.4. Text – Readme Files; Final data analysis products – presentations, reports.
12.3. Literate programming and version control
12.3.1. R markdown
12.3.2. Python notebook

DATA warehousing

1.0 Data Warehousing Management (16) Section 1 Total
    1.1 Data Warehousing Project (4) D3
    1.2 Data Warehousing Program (8) D3
    1.3 Data Warehousing Roles (4) D3
2.0 Data Warehousing Infrastructure Architectures (11) Section 3 Total
    2.1 Enterprise Architectures for Warehouse Planning (2) D4
    2.2 Data Warehousing Architectures (9) D4
3.0. Tools and Technology Types (13) Section 5 Total
    3.1. Data Warehousing (8) D3
    3.2 Business Intelligence (5) D3
4.0 Data Warehousing Analysis (13) Section 6 Total
    4.1 Requirements Analysis (9) D5
    4.2 Source Data Analysis (4) D4
5.0 Warehousing Data Modeling and Database Design (18) Section 7 Total
    5.1 Model Types and Components (9) D5
    5.2 Data Modeling for the Data Warehouse (9) D5
6.0 Data Integration (13) Section 7 Total
    6.1 ETL System Functionality (5) D4
    6.2 ETL and Alternative Data Integration Development (8) D4
7.0 Other Warehousing Development Activities (6) Section 8 Total
    7.1 Warehousing Applications and Databases (4) D3
    7.2 Warehousing Training and Documentation (2) D3
8.0 Implementation and Ongoing Activities (10) Section 9 Total
    8.1 Deployment (5) D3
    8.2 Ongoing Support and Maintenance (5) D3

IS Management

1. Management and Information Systems Decision Concepts
     1.1 Management Functions and IS Business Drivers
     1.2 IS Decisions and Skills Required
2. Strategies and Business Process Management (BPM)
     2.1 IS Influences on Strategies
     2.2 IS Influences on Strategies
3. Architecture and Infrastructure
     3.1 Enterprise Architecture
     3.2 IT Architecture
4. IS / IT Sourcing and Funding
     4.1 IS / IT Sourcing
     4.2 IS / IT Funding
5. IS / IT Organization, Governance and Ethics
     5.1 IS Organization
     5.2 IS / IT Governance, Ethics and Privacy
6. Project Management
     6.1 Project and Its Management
     6.2 Project Methodologies and Techniques
7. Managing Data, Information, and Knowledge, and Using Business Analytics
     7.1 Data, Information and Knowledge
     7.2 Business Intelligence and Business Analytics

IT Consulting

1.0 Consulting Function (25%)
    1.1 Planning (5)
    1.2 Business Operations (10)
    1.3 Consulting Skills (10)
2.0 Consulting Work Acquisition (15%)
    2.1 Marketing (5)
    2.2 Sales (4)
    2.3 Proposals (3)
    2.4 Contracts (3)
3.0 Consulting work management (15%)
    3.1 Project management (7)
    3.2 Time management (6)
    3.3 Client Roles (2)
4.0 Client Management (20%)
    4.1 Organizational awareness (6)
    4.2 Maintaining client relationships (6)
    4.3 Client’s Business Environment (5)
    4.4 Client Education (3)
5.0 Ethical Guidelines and Professional Standards (25%)
    5.1 Ethical Issues (20)
    5.2 Guidelines and Standards (5)

Microcomputers and Networking

1. Resource Management Functions
    1.1 General Administration
    1.2 Technical Administration
    1.3 End User Support
2. Microcomputer Architecture
    2.1 System Unit
    2.2 Peripherals
3. Microcomputer Software
    3.1 Applications
    3.2 Systems Software
4. Network Technology
    4.1 Networking Concepts
    4.2 Local Area Networking (LAN)
    4.3 Wide Area Networking (WAN)
    4.4 Value Added Networks (VAN)

Object Oriented Analysis and Design

1. Object Theory (10%)
    1.1 Definition of objects
    1.2 Objects in the data world
    1.3 Methods
2. Models and Modeling (10%)
    2.1 Designing the Model
    2.2 Assigning Object Responsibilities
    2.3 Designing The Classes
    2.4 Building Applications
    2.5 Extending the System
3.   Objects and Classes (25%)
    3.1 Abstraction & encapsulation
    3.2 Composition
    3.3 Inheritance
    3.4 Classification
    3.5 Polymorphism
    3.6 Overloading
4. Object Models (25%)
    4.1 Definition and background
    4.2 Essential Elements
    4.3 Design Considerations
    4.4 Advantages and disadvantages of using object models
5. Development Methodologies - historical perspective (5%)
6. OODLC (25%)
    6.1 Analysis
    6.2 Design
    6.4 Testing
    6.5. Maintenance
    6.6 Security & disaster planning

office information systems

1. Office Environment
    1.1 Centralization/Decentralization - Issues for Work Groups and Systems
    1.2 Environmental Engineering for Efficiency
    1.3 Technology Evaluation
2. Office Technologies
    2.1 Internal/External Communications
    2.2 Image
    2.3 Storage Media
    2.4 Public Access Technologies
    2.5 Installation, Maintenance and Security of Information Systems
    2.6 Records Management
    2.7 Managing to Prevent Obsolescence
3. End-User Computing
    3.1 Product Evaluation, Analysis and Support
    3.2 Information Center
    3.3 Coordinating and Supporting End-User Application Development
    3.4 Managing Resistance

Procedural (Advanced) programming

1. Data and File Organization
    1.1 Data Formats, Internal and External
    1.2 Data Structures
    1.3 File Structures
    1.4 Database Models
2. Program Design
    2.1 Process
    2.2 Methods
    2.3 Representation
3. Procedural Programming Structure
    3.1 Data Definition
    3.2 Control Structures
    3.3 Subprograms
4. Procedural Programming Considerations
    4.1 Order of Implementation
    4.2 Exception and Interrupt Handling
    4.3 Style
    4.4 Program Efficiency
    4.5 Testing and Debugging
    4.6 Maintenance Procedures
    4.7 Fundamental Algorithms
5. Integration with Hardware and Software
    5.1 Hardware Components
    5.2 Language Paradigm Selection
    5.3 Utilities
    5.4 Operating Systems Interface
    5.5 Communications and Distributed Processing

PUBLIC SECTOR dATA GOVERNANCE

1.0 Data Governance Concepts and Mission Drivers (15%)
1.1 Concepts & Definitions
1.1.1 Definitions
1.1.2 Key Public Sector Concepts
1.2 Governance Organizational Structures
1.2.1 Data Governance Council
1.2.2 Centralized vs. Federated Governance Models
1.3 Mission Drivers
1.3.1 Data as a democratizer
1.3.2 Data as enabler of good government
1.3.3 Data as a national security issue
1.4 Data Governance & Stewardship Management Tools
1.4.1 Meta-data tools and repositories
1.4.2 Data asset inventory
1.4.3 Data modeling tools
1.4.4 Communication tool suite
2.0 Legal and Regulatory Environment (20%)
2.1 Federal data governance statutes
2.1.1 Freedom of Information Act
2.1.2 US Privacy Act
2.1.3 Health Insurance Portability and Affordability Act
2.1.4 Federal Records Act
2.1.5 Federal Information Security Modernization Act
2.2 Regulatory frameworks
2.2.1 Executive Order 13556 on Controlled Unclassified Information
2.2.2 Executive Order 13526 on National Security Information
2.2.3 2013 Open Data Policy
2.2.4 Commerce Department Privacy Shield (Global universal data privacy regulations)
2.3 Data Sharing & Ethics
3.0 Data Governance and Stewardship Roles & Responsibilities (20%)
3.1 Data Governance Roles
3.1.1 The Role of Chief Data Officer (CDO)
3.1.2 Composition of a Public Sector Data Governance Council
3.1.3 Data Owner
3.1.4 Data Steward
3.1.5 Data Custodian
3.1.6 Data Curator
3.2 Public Sector Stakeholder Management
3.2.1 Data Providers
3.2.2 Data Users
3.2.3 Education and Training of Stakeholders
4.0 Data Governance Inputs and Deliverables (20%)
4.1 Inputs
4.2 Primary Deliverables
4.2.1 Data Governance Policies
4.2.2 Data Governance Standards and Procedures
4.2.3 Data Governance Goals and Outputs
5.0 Fundamental Knowledge Areas for Public Sector Data Stewardship (25%)
5.1 Data Preservation Planning
5.2 Data Access and Security Issues
5.2.1 Concepts and Terms
5.3 Reference and Master Data Management
5.3.1 Key Concepts
5.3.2 MDM tools and methodologies
5.4 Metadata Management
5.4.1 Concepts and Terms
5.4.2 Stewardship Activities
5.5 Document and Content Management
5.5.1 Key Concepts
5.6 Data Warehousing and Cloud Solutions
5.6.1 Concepts and Terms
5.6.2 Stewardship Activities
5.6.3 Risk/Reward Calculations
5.7 Data Quality Management
5.7.1 Concepts and Terms
5.7.2 Activities

SOFTWARE ENGINEERING

1. Computer System Engineering
    1.1 Computer-Based Systems
    1.2 Computer-System Life Cycle Modeling
  1.3 Hardware Considerations
    1.4 Software Considerations
    1.5 Human Considerations
2. Software Project Planning
    2.1 Project Planning Objectives
    2.2 Software Scope
    2.3 Resources
    2.4 Metrics for Software Productivity and Quality
    2.5 Software Project Estimation
    2.6 Decomposition Techniques
    2.7 Empirical Estimation Models
    2.8 Automated Estimation Tools
    2.9 Software Project Scheduling
    2.10 Software Acquisition
    2.11 Organizational Planning
    2.12 The Software Project Plan
3. Software Requirements
    3.1 Analysis Principles
    3.2 Object-Oriented Analysis
    3.3 Software Prototyping
    3.4 Systems Analysis
    3.5 Requirements Analysis Methodologies
    3.6 Data Flow-Oriented Analysis Methods
    3.7 Data Structure-Oriented Methods
    3.8 Data Structured Systems Development
    3.9 Jackson System Development
    3.10 Automated Tools for Requirements Analysis
4. Software Design
    4.1 The Design Process
    4.2 Design Fundamentals
    4.3 Modular Design
    4.4 Data Flow-Oriented Design
    4.5 Data Structure-Oriented Design
    4.6 Object-Oriented Design
    4.7 Real Time Design
    4.8 Model-Based Design
    4.9 Procedural Design
    4.10 Design Documentation
5. Programming Languages and Coding
    5.1 The Translation Process
    5.2 Programming Language Characteristics
    5.3 Programming Language Fundamentals
    5.4 Language Classes
    5.5 Programming Aids
    5.6 Coding Style
    5.7 Efficiency
6. Software Quality Assurance
    6.1 Software Quality and Quality Assurance
    6.2 Software Reviews
    6.3 Formal Technical Reviews
    6.4 Software Quality Metrics
    6.5 Software Reliability
    6.6 Software Quality Assurance Approach
7. Software Testing Techniques
    7.1 Software Testing Fundamentals
    7.2 White Box Testing
    7.3 Basis Path Testing
    7.4 Loop Testing
    7.5 Black Box Testing
    7.6 Proof of Correctness
    7.7 Automated Testing Tools
    7.8 Strategic Approach to Software Testing
    7.9 Unit Testing
  7.10 Integration Testing
    7.11 Validation Testing
    7.12 System Testing
    7.13 Debugging
8. Software Maintenance and Configuration Management
    8.1 Maintenance Characteristics
    8.2 Maintainability
    8.3 Maintenance Tasks
    8.4 Maintenance Side Effects
    8.5 Software Configuration Management

Systems Programming (Operating Systems)

1. Languages
    1.1 Assembly Language Concepts
    1.2 Higher Level Language Structures
2. Operating Systems
    2.1 Processor Dispatching
    2.2 Interrupt Handling
    2.3 Paging Supervisor
    2.4 Resource Allocation
    2.5 Input/Output Spooling
    2.6 Operator Communication
    2.7 Program Loading
    2.8 Memory Protection and Privileged Instructions
3. Language Processing
    3.1 Parsing and Syntactic/Semantic Analysis
    3.2 Code Generation and Optimization
    3.3 Module Collection and Address Resolution
    3.4 Development Techniques
4. Concurrent and Distributed Processing
    4.1 Communication Protocols
    4.2 Network Architecture
    4.3 Multi-Tasking
    4.4 Dynamic Resource Allocation
    4.5 Fault-Tolerance and Recovery
    4.6 Security
5. Data Management Systems
    5.1 Physical Data Structure
    5.2 Logical Data Models
    5.3 Concurrent Access Control
    5.4 Data Integrity
6. Computer Architecture and Implementation
7. Performance Evaluation
    7.1 Performance Measurement
    7.2 Modeling and Simulation
    7.3 Tuning
8. Software Tools
9. System Management
    9.1 Security
    9.2 Software Installation
    9.3 Software Tailoring

sYSTEMS DEVELOPMENT

1. Systems Analysis
    1.1 General System Theory
    1.2 Preliminary Studies
    1.3 Definition of Objectives
    1.4 Data Gathering and Analysis
    1.5 System Requirements
2. Systems Design and Implementation
    2.1 Alternative Systems Design
    2.2 Logical Design
    2.3 Detailed Design
    2.4 Privacy, Security and Controls
    2.5 System Implementation
    2.6 System Evaluation and Maintenance
3. The Systems Analyst as a Professional
    3.1 Organizational Roles of the Systems Professional
    3.2 Interpersonal Roles of the Systems Professional
    3.3 Communication Skills
    3.4 Identifying Key Individuals

sYSTEMS SECURITY

1. Risk Assessment
    1.1 Organization
    1.2 Systems and Data Asset Valuation
    1.3 Threat Characteristics
    1.4 Risk Assessment
    1.5 Dealing with Risk
2. Recovery from Information Service Interruptions
    2.1 Recoverable Storage Management
    2.2 Business Continuity Planning
    2.3 Disaster Management
3. Information and System Security
    3.1 Telecommunications
    3.2 Database Security
    3.3 Cryptography
    3.4 Operating Systems
    3.5 Microcomputers and Local Area Networks
    3.6 Physical Security
4. Security in System Design
    4.1 System Security Objectives and Functions
    4.2 Data Integrity Assurance
    4.3 Life Cycle Approach
5. Security Management
    5.1 Policy Setting, Implementation and Administration
    5.2 Security Awareness
    5.3 Information Ethics
    5.4 Personnel Issues
    5.5 Evaluation of Security Measures

WEB DEVELOPMENT

1. General Page Design Concepts
    1.1 Wireframes
    1.2. Cascading Style Sheets (CSS)
    1.3. Color Theory
    1.4 Aspect ratio for screens
    1.5 Display resolution
    1.6 Bgcolor
    1.7. Background image
    1.8. Accessibility
2. General Server Concepts
    2.1. Clustering
    2.2 HTTP Server Software
    2.3. Front Page Server Extensions
    2.4 Common Gateway Interface (CGI)
    2.5 Content Management System
    2.6 Application Service Provider (ASP)
3. Development Tools/Environments
    3.1. Browser
    3.2 Development Environments/Tools
4. Programming Languages
    4.1 HyperText Markup Language (HTML)
    4.2 Dynamic HTML (DHTML)
    4.3 Extensible Markup Language (XML)
    4.4 Extensible Style Sheet Language (XSL)
    4.5 Languages: server-side, client-side
    4.6. Web Service Description Language (WSDL)
    4.7 PHP: Hypertext Preprocessor
    4.8 Cold Fusion Markup Language (CFML) / .CFM files
5. Programming Concepts
    5.1. Server-side script
    5.2 Client-side script
    5.3 Round-trips
   5.4 Form validation
    5.5 Query strings
    5.6 Object-oriented programming (OOP)
    5.7 Coupling
    5.8 Cohesion
    5.9 Parameters, Arguments
    5.10. Modular design
    5.11 Information hiding
    5.12 Three-tiered/layered model
    5.13 Web Services
    5.14 Simple Object Access Protocol (SOAP)
    5.15 Java 2 Enterprise Edition (J2EE)
    5.16 Active Server Pages (ASP)
    5.17 Distributed Object Models
6. Project Management
    6.1 Life cycle approaches
    6.2 Versioning
    6.3 Source Code Control System
    6.4 Software Quality Assurance
7. Site Management and Security
    7.1 Demilitarized zone
    7.2 Firewall
    7.3 Proxy Server
    7.4 Authentication
    7.5 Copyright
    7.8 Statistics and reporting
8. Database and Data Access
    8.1. Common Packages
    8.2 Relational Model
    8.3 Normalization
    8.4 Data access technologies
    8.5 SQL
    8.6 Stored Procedures
9. Internet and Communications Concepts
    9.1 TCP/IP
    9.2 Uniform Resource Locator (URL)
    9.3 World Wide Web Consortium (W3C)
    9.4 Request For Comments (RFC)
    9.5 Universal Description, Discovery and Integration (UDDI)
    9.6 HyperText Transfer Protocol (HTTP)
    9.7 HTTP Secure (HTTPS)
    9.8 Wireless Application Protocol (WAP)
  9.9 File Transfer Protocol (FTP)
    9.10 Certificate Authority
    9.11 Registrar
    9.12 Domain Name System
    9.13 Virtual Private Network (VPN)
    9.14 Intranet
    9.15 Internet Service Provider (ISP)
    9.16 Wide Area Network (WAN)
    9.17 Local Area Network (LAN)
    9.18 Bandwidth and connection speed
10. File Formats Related to Web Development
    10.1 Streaming media
    10.2 Non-streaming media
    10.3 Additional formats

Zachman enterprise framework

1.0 Zachman Enterprise Architecture Framework Concepts and Roles Total items (15)
    1.1 Zachman Framework Concepts (13)
    1.2 Zachman Framework Organizational Roles (2)
2.0 Planning for Zachman Enterprise Architecture Framework Total items (18)
    2.1 Initial Enterprise Architecture Effort (10)
    2.2 Ongoing Enterprise Architecture Programs / Initiatives (8)
3.0 Zachman Framework Models Principles Total items (47)
    3.1. Enterprise Architecture Fundamentals (10)
    3.2 Zachman Framework Models (20)
    3.3 Zachman Framework Perspectives (17)
4.0 Zachman Framework Infrastructure Management Total items (20)
    4.1. Standards and Rules (15)
    4.2 Model Management (5)

Blockchain Examination - Technical

Blockchain Technical Examination
1. Blockchain Fundamentals
a. Cryptographic primitives
i. Hash Functions & Properties
ii. Digital Signatures
b. Hash Pointers and Data Structures
c. Digital Signatures
d. Public Keys as Identities
e. Simple Cryptocurrencies
f. Blockchain and Anonymity
i. Anonymity Basics
ii. How to de-anonymize coins
iii. Mixing
iv. Decentralizing Mixing
v. ZeroCoin and ZeroCash
vi. Tor and th Silk Road
g. The Bitcoin Platform
i. Bitcoin as an Append Only Log
ii. Bitcoin as Smart Property
iii. Secure Multi-Party Lotteries in Bitcoin
iv. Bitcoin as a Randomness Source
v. Prediction Markets & Real-World Data Feeds
vi. Counterparty (a bitcoin aware platform) and how it extends
2. Elliptical Curve Cryptology ECC and ECDSA
a. How does it work?
b. Where used
c. Benefits offered to the system
3. Proof of Work
a. Function
b. Bitcoin implementation
c. SHA-256
d. 51% attack or Sybil attack – filling the network with enough clients/nodes that you can
isolate individuals from entering (Oligopoly/Cartel)
e. What is a coin?
i. Incremented integer, wallet repository holding the public key – hash map of the
public key mapped to the number value of the number of token held
ii. e.g. 0xA56549 | 17.1234567 (1 satoshi represents 1/100m of coin) Hex Coins held
f. Mining and implications of Proof of Work
i. The task of Miners
ii. Mining Hardware
iii. Energy Consumption and Ecology
iv. Mining Pools
v. Mining Incentives and Strategies
vi. Alternative Mining Puzzles
> Essential Puzzle Requirements
> ASIC Resistant Puzzles
> Proof-of-useful-work
> Non-outsourceable Puzzles
> Proof-of-Stake “minting of coins” – a pseudo random selection of who generates the next block based on “a stake” in the chain
4. What is Proof of Stake?
5. What is a smart contract?
6. What are properties of blocks, block-headers, and transactions?
7. Cold versus hot storage
8. Wallets, storage and physical security
i. Multisig versus Single Signature
9. How is the blockchain stored?
10. How do you have light weight nodes on the network?
11. What is the ERC-20 standard?
i. Abstract interface that coins on the Ethereum blockchain have to implement
ii. Balance transfer withdraw and random
12. What is the Merkle Root and its Hash
13. What is the double-spend problem and how is resolved?
14. How does difficulty scale on a blockchain?
15. In Bitcoin what is the difficulty scale algorithm (2016 blocks) every two weeks
16. How often are blocks added to the blockchain? Every 10 minutes
17. Block size is currently 2MB and why are they likely to increase e.g. 20MB?
18. What are transaction fees and who is paid transaction fees (miners)?
19. What is the address base for a blockchain 160 bits number (2^160) public addresses available
16commas in the number. 400billion trillion trillion
20. What is a release schedule algorithm for coins?
21. What happens if you send coins to an address that hasn’t been claimed.
i. You can send coins to any address and getting it back is impossible (non-repudiable)
ii. Ransomware issues (random address generation and if money arrives, ransoms -haha)
22. Are transactions reversible?

Blockchain Examination

0.0.0 Blockchain Technical Exam
1.0.0 Introduction to Blockchain
   1.1.0 Fundamental Concepts of Blockchain & Architecture
2.0.0 Introduction to Hyperledger Projects
   2.1.0 Overview of Hyperledger Projects and Tools
3.0.0 Hyperledger Fabric Architecture and Components
   3.1.0 Hyperledger Fabric Overview
   3.2.0 Hyperledger Fabric Model
4.0.0 Developing Smart Contracts with Hyperledger Fabric
   4.1.0 Smart Contract Development
5.0.0 Smart Contract Invocation
   5.1.0 Overview of fabric -samples, Fabcar and the Test Network
   5.2.0 Deploying a Smart Contract
   5.3.0 Invoking Smart Contract Transactions
6.0.0 Testing and Maintenance
   6.1.0 Creating a Fabcar UI Client
   6.2.0 Performing Rapid Smart Testing
   6.3.0 Identifying and Reviewing Logs
   6.4.0 Creating Unit Test Contracts
7.0.0 Blockchain Supply with Hyperledger using DApp with Fabric
   7.1.0 Designing a Blockchain Supply Chain
   7.2.0 Writing Chaincode as a Smart Contract
   7.3.0 Compiling and Deploying Fabric Chaincode
   7.4.0 Running and Testing the Smart Contract
   7.5.0 Developing an Application with Hyperledger Fabric Through the SDK
8.0.0 Hyperledger Fabric-on Cloud
   8.1.0 Deploying Hyperledger on Amazon Blockchain Services
   8.2.0 Using the IBM Cloud for Blockchain Applications
   8.3.0 Oracle Blockchain Platform Overview and Use
9.0.0 Hyperledger V.2 Integration
   9.1.0 New Features of Hyperledger Fabric V.2
   9.2.0 Updating the Capability Level of a Channel
   9.3.0 Considerations for Moving to V2
10.0.0 Overview of 2 Hyperledger Projects
10.1.0 Hyperledger Aries
10.2.0 Hyperledger Avalon
11.0.0 Overview of 2 Other Hyperledger Projects
11.1.0 Hyperledger Besu
11.2.0 Hyperledger Grid
12.0.0 Emerging Applications of Blockchain
Reference: Blockchain with HyperLedger

PROGRAMING LANGUAGE EXAM OUTLINES

C LANGUAGE

1. Data Types
2. Operators and Expressions
3. Control Flow
4. Functions
5. Pointers and Arrays
6. Structures and Unions
7. Standard I/O Library
8. Library Functions and Environment
9. The Preprocessor

c++ LANGUAGE

1. B1. Basic Language Elements
2. Expressions and Operators
3. Flow Control
4. Arrays and Pointers
5. Object Oriented Programming
6. Functions
7. Exception Handling
8. Standard Libraries
9. The Preprocessorasic Language Elements
2. Expressions and Operators
3. Flow Control
4. Arrays and Pointers
5. Object Oriented Programming
6. Functions
7. Exception Handling
8. Standard Libraries
9. The Preprocessor

COBOL LANGUAGE

1. General
2. Data Description
3. Data Manipulation
4. Input/Output
5. Flow of Control
6. Other Language Features

JAVA LANGUAGE

1. Java Technology
        1.1 Fundamental concepts Java Programming Language
        1.2 Key Java technology groups
2. Analyzing Problems and Designing Solutions
        2.1 Object Oriented Analysis and Design
        2.2 Designing Object Classes
3. Developing and Testing Java Technology
        3.1 Four components of a class in Java
        3.2 Testing classess using the main method in Java
        3.3 Compiling and executing Java programs
4. Declaring, Intializing, and using Variables
        4.1 Identify, define and use variables in Java
        4.2 Primitive data types
        4.3 Declarations, initialization, and use of variables and constants.
        4.4 Modify values in variables using operators
        4.5 Promotion and type casting
5. Creating and using Objects
        5.1 Declare, instantiate and intialize object reference variables
        5.2 Use String class as specified in the JDK
        5.3 Use Java 2 Platform, Standard Edition (J2SE) class library
6. Operators and Decision Constructs
        6.1 Relational and conditional operators
        6.2 IF and IF ElSE constructs
        6.3 Switch construct
        6.4 While and Do/While Loops
7. Methods
        7.1 Advantages of methods: worker and calling methods
        7.2 Declaring and invoking methods
        7.3 Object and static methods
        7.4 Overloading methods
8. Encapsulation and Constructors
        8.1 Encapsulating to protect data
        8.2 Creating constructors to initialize Objects
9. Arrays
        9.1 One-dimensional arrays
        9.2 Setting array values using length and a loop
        9.3 Pass arguments to main method for use in a program
        9.4 Two-dimensional and multi-dimensional arrays
        9.5 Copying array values from one array to another
10. Inheritance
        10.1 Defining and using inheritance
        10.2 Abstraction
        10.3 Class libraries and use in code
11. Object-oriented Programming
        11.1 Modeling concepts: abstraction, encapsulation and packages
        11.2 Code reuse in Java
        11.3 Class, member, attribute, method, constructor, and package
        11.4 Access modifiers: Public and Private
        11.5 static variables, methods, and initializers
        11.6 Final classes, methods and variables
        11.7 Interfaces, enumerated types, static import statement

JAVASCRIPT LANGUAGE

1.0.0 JavaScript Technology
2.0.0 Analyzing Problems and Designing Solutions
3.0.0 Developing and Testing JavaScript Technology
4.0.0 Declaring, Initializing, and using Variables
5.0.0 Creating and using Objects
6.0.0 Operators and Decision Constructs
7.0.0 Methods
8.0.0 Encapsulation and Constructors
9.0.0 Arrays
10.0.0 Inheritance
11.0.0 Object-oriented Programming
12.0.0 Advanced programming with JavaScript
13.0.0 Frameworks

1. Information Systems Core
1.0 Information Systems   (20%) (D3)
Organizational Role, Business Drivers, Strategy, Structure, Acquisition & Management
1.1 The Role of IS in the Organization
1.2 Business Drivers and Information Systems Valuation
1.3 Structuring the IS Organization
1.4 Acquiring Information Technology Resources and Capabilities (In-house Development, External Acquisition, or Outsourcing)
1.5 Information Systems Technology Components and System Types
2.0 Data and Information Management (15%) D3
This section covers the core concepts in data and information management using conceptual data modeling techniques. This section also includes coverage of basic database administration tasks and key concepts of data quality and data security.
2.1 Data Management
2.2 Data Governance
2.3 Data Modeling
2.4 Meta-data Management
2.5 Data Quality Management
2.6 Data Security Management
2.7 Database Approach
3.0 Enterprise Architecture (15%)    D3
This section covers the design, selection, implementation and management of enterprise IT solutions. The focus is on frameworks and strategies for infrastructure management, system administration, data/information architecture, content management, distributed computing, middleware, legacy system integration, system consolidation, software selection, total cost of ownership calculation, IT investment analysis, and emerging technologies.
3.1 Enterprise Architecture Types and Structures
3.2 Enterprise Architecture Frameworks, Methodologies, and Measurements
4.0 IT Infrastructure (15%)    D3
This section covers topics related to both computer and systems architecture and communication networks, with an overall focus on the services and capabilities that IT infrastructure solutions enable in an organizational context.
4.1 Systems Concepts
4.2 Operating Systems
4.3 Networking
4.5 IT Control and Service Management Frameworks (COBIT, ITIL, etc.)
4.6 Managing IT Infrastructure and Business continuity
5.0 IS Project Management (15%)    D3
Project management in the modern organization is a complex team-based activity, where various types of technologies (including project management software as well as software to support group collaboration) are an inherent part of the project management process. This section focuses on a systematic methodology for initiating, planning, executing, controlling, and closing projects.
5.1 Introduction to Project Management
5.2 The Project Management Lifecycle
5.3 Managing Project Teams
5.4 Project Initiation and Planning
5.5 Managing Project Scheduling
5.6 Managing Project Resources
5.7 Managing Project Quality and Risk
5.8 Systems Procurement
5.9 Project Execution, Control and Closure
6.0 Systems Analysis & Design (15%)    D3
This section focuses on a systematic methodology for analyzing a business problem or opportunity, determining what role, if any, computer-based technologies can play in addressing the business need, articulating business requirements for the technology solution, specifying alternative approaches to acquiring the technology capabilities needed to address the business requirements, and specifying the requirements for an information systems solution.
6.1 Business Process Management
6.2 Structuring of IT-based Opportunities into Projects
6.3 Analysis and Specification of System Requirements
6.4 Implementation Strategies

2-5: See under Foundations Examinations

34. Big Data Examination
1. Big Data Terminology

a. Volume, Variety, Velocity, Veracity
b. Multi-structured data
c. Data Ingestion

2. Big Data Concepts

a. Discovery, Search, Navigation
b. Land Data (Access, Collect), Manage, Store
c. Multi-source integration
d. Big Data Analytics
e. Structured controlled data
f. Analyzing unstructured data
g. Real time (Streaming data analytics)
h. Full Contextual Analysis
i. Textual Disambiguation
j. Big Data Integration

3. Technical Considerations

a. Storage volume and relationships
b. Data Architecture
c. Data Ingestion Requirements
d. Infrastructure considerations
e. Feature extraction and metadata
f. Consistency/Redundancy
g. Implementation considerations

4. Big Data Governance - sources, data quality, integration, security, life cycle, master data
management & definitions

a. Organizational Structures and Awareness
b. Stewardship
c. Policy
d. Value Creation
e. Fit of Data
f. Data Risk Management & Compliance
g. Information Security & Privacy
h. Data Quality management
i. Classification and metadata
j. Information lifecycle management
k. Audit information, logging and reporting

35. Blockchain Technical
Blockchain Technical Examination
1. Blockchain Fundamentals
a. Cryptographic primitives
i. Hash Functions & Properties
ii. Digital Signatures
b. Hash Pointers and Data Structures
c. Digital Signatures
d. Public Keys as Identities
e. Simple Cryptocurrencies
f. Blockchain and Anonymity
i. Anonymity Basics
ii. How to de-anonymize coins
iii. Mixing
iv. Decentralizing Mixing
v. ZeroCoin and ZeroCash
vi. Tor and th Silk Road
g. The Bitcoin Platform
i. Bitcoin as an Append Only Log
ii. Bitcoin as Smart Property
iii. Secure Multi-Party Lotteries in Bitcoin
iv. Bitcoin as a Randomness Source
v. Prediction Markets & Real-World Data Feeds
vi. Counterparty (a bitcoin aware platform) and how it extends
2. Elliptical Curve Cryptology ECC and ECDSA
a. How does it work?
b. Where used
c. Benefits offered to the system
3. Proof of Work
a. Function
b. Bitcoin implementation
c. SHA-256
d. 51% attack or Sybil attack – filling the network with enough clients/nodes that you can
isolate individuals from entering (Oligopoly/Cartel)
e. What is a coin?
i. Incremented integer, wallet repository holding the public key – hash map of the
public key mapped to the number value of the number of token held
ii. e.g. 0xA56549 | 17.1234567 (1 satoshi represents 1/100m of coin) Hex Coins held
f. Mining and implications of Proof of Work
i. The task of Miners
ii. Mining Hardware
iii. Energy Consumption and Ecology
iv. Mining Pools
v. Mining Incentives and Strategies
vi. Alternative Mining Puzzles
> Essential Puzzle Requirements
> ASIC Resistant Puzzles
> Proof-of-useful-work
> Non-outsourceable Puzzles
> Proof-of-Stake “minting of coins” – a pseudo random selection of who generates the next block based on “a stake” in the chain
4. What is Proof of Stake?
5. What is a smart contract?
6. What are properties of blocks, block-headers, and transactions?
7. Cold versus hot storage
8. Wallets, storage and physical security
i. Multisig versus Single Signature
9. How is the blockchain stored?
10. How do you have light weight nodes on the network?
11. What is the ERC-20 standard?
i. Abstract interface that coins on the Ethereum blockchain have to implement
ii. Balance transfer withdraw and random
12. What is the Merkle Root and its Hash
13. What is the double-spend problem and how is resolved?
14. How does difficulty scale on a blockchain?
15. In Bitcoin what is the difficulty scale algorithm (2016 blocks) every two weeks
16. How often are blocks added to the blockchain? Every 10 minutes
17. Block size is currently 2MB and why are they likely to increase e.g. 20MB?
18. What are transaction fees and who is paid transaction fees (miners)?
19. What is the address base for a blockchain 160 bits number (2^160) public addresses available
16commas in the number. 400billion trillion trillion
20. What is a release schedule algorithm for coins?
21. What happens if you send coins to an address that hasn’t been claimed.
i. You can send coins to any address and getting it back is impossible (non-repudiable)
ii. Ransomware issues (random address generation and if money arrives, ransoms -haha)
22. Are transactions reversible?

6. Business Intelligence and Data Analytics

Exam Outline
1.0 Business Intelligence Concepts & Roles (10)
     1.1 Definitions and Business Drivers (5)
     1.2 Business Intelligence in Organizational Roles (5)
2.0 Business Management Perspectives (15)
     2.1 Business concepts principles and guidelines (4)
     2.2 Performance Management (5)
     2.3 Ongoing monitoring and controlling execution (6)
3.0 Analytics Techniques and Usage (65)
     3.1 Modeling (10)
     3.2 Business and Data Analysis (14)
     3.3 Data Visualization Techniques (4)
     3.4 Statistics (14)
     3.5 Assessments (4)
     3.6 Measurements and monitoring (5)
     3.7 Decision making (14)
4.0 Business Intelligence / Decision Support Systems (10)
     4.1 Front end business intelligence technologies (6)
     4.2 Back end tools, applications and technologies (4)

7. Business Technology Management

Exam Outline
1. Business Systems (15%), D3
   1.1 Business Functions      1.2 Organizational Performance (Assets Management)
     1.3 Enterprise Architecture
2. Management Systems (10%), D3
    2.1 Strategic, Tactical and Operational Systems
    2.2 Planning, Organizing, Controlling and Coordination
    2.3 Optimization: Storage, Production, Distribution, Services & Resources Management
    2.4 Quality and Process Management
    2.5 Organizational Transformation & Change Management
3. Planning (15%), D3
   3.1 Measuring and Management
      3.2 Business Process Management and Continuous Improvement
      3.3 Analytics (Quantitative and Qualitative)
      3.4 Research and Development
4. Administration and Decision Making (10%), D3
   4.1 Decisions Support
    4.2 Management Roles & Responsibilities
5. Control and Coordination (20%), D3
   5.1 Programs
    5.2 Projects
    5.3 Scope
    5.4 People
    5.5 Fixed Assets and Equipment
    5.6 Budgeting, Cash Flow, Cost, Investment & ROI
    5.7 Risk
    5.8 Time
6. Business Information Systems and Technology (30%), D3
   6.1 Business Data (Organizing, Access, Storage, Protection, Archiving, Big Data)
   6.2 Business Processes
   6.3 Systems Architectures
   6.4 Technologies

8. Business Information Systems Exam

The Business Information Systems exam is intended to help define the body of knowledge and professional practices associated with the development and management of Business Information Systems. The BIS exam is designed to test the candidate's \knowledge of the usage of Information Systems theory and practice at a level of competency appropriate to senior IS professionals.
Exam Outline
1. Business Information Systems Applications
     1.1 Financial Planning/Decision Support
     1.2 Accounting
     1.3 Organizational Performance
     1.4 Marketing and Sales
     1.5 Materials Management
     1.6 Production and Distribution Management

2. The Business Information Systems Environment
     2.1 System Analysis/Design Function
     2.2 Data Base Design Function
     2.3 Application Programming Function
     2.4 Computer Operations Function
     2.5 Systems Programming Function
     2.6 Quality Control Function
     2.7 Information Center Function

3. Business Information System Considerations
     3.1   User/IS Relations
     3.2   Business Economics
     3.3   IS Resource Management
     3.4   EDP Equipment Use
     3.5   Software Development Environment

9. Business Process Management Exam

Exam Outline
1.0 Business Process Management Concepts & Roles (15) Section 1 Total
    1.1 Definitions (6) D3
    1.2 BPM Organizational Roles & Responsibilities (9) D3
2.0 Business Management Perspectives (20) Section 2 Total
    2.1 Business Concepts, Principles and Guidelines (6) D3
    2.2 Performance Management (7) D4
    2.3 Ongoing Monitoring and Controlling Execution (7) D4
3.0 BPM Methodology Approaches and Techniques (40) Section 2 Total
    3.1 Enterprise Process Planning (6) D4
    3.2 Process Analysis and Design (24) D4
    3.3 Process Management Improvement (10) D4
4.0 Business Process Management Technology (25) Section 4 Total
    4.1 Business Process Management Systems (BPMS) Implementation (10) D3
    4.2 BPMS Technology Types (15) D4

9A. Cyber Security Exam
Exam Outline

Foundations of Cyber Security (10%)
1. The importance of information security in business continuity.
2. Security threats and managerial controls to counter threats
3. Procedures for ensuring compliance with security policies, standards, plans for security implementation
4. Legal implications of security standards and implementation
5. Sourcing, managing and implementing an effective security program
6. Audit, recovery and coping with security breaches
Computer Hacking Forensic Investigators (24%)
1. Function and limitations of forensic investigations.
2. Procedures used in conducting forensic investigations.
3. Legal issues of preparing for and performing digital forensic analysis.
4. Digital evidence – storage, preparation and requirements.
5. Digital forensic lab and digital forensics tools
Ethical Hacking (11%)
1. What constitutes ethical hacking
2. Footprinting and reconnaissance
3. Scanning networks, penetration testing
4. Enumeration
5. Hacking
  1. system (OS, wired and wireless),
  2. session
  3. webserver
  4. web application
  5. SQL insertions
  6. Buffer overflow
  7. Encryption – Decryption
6. Trojans, backdoors, viruses, worms, sniffers
7. Social engineering, denial of service
8. Evading IDS, Firewalls, and Honeypots
Incident response handling and disaster recovery (14%)
1. Information security risk and risk management
2. Planning for organizational readiness
3. Contingency strategies
4. Incident response and recovery
  1. Detection & decision making
  2. Organizing and preparing for CSIRT
  3. Response strategies
  4. Recovery and maintenance
5. Disaster Recovery:
  1. Preparation and implementation
  2. Operation and maintenance
6. Business continuity planning
7. Crises management and international standards
Network Security Administration (17%)
1. Networks and Security issues
2. Malware and social engineering attacks
3. Data, Application and Network based attacks
4. Cryptography and de-encryption
5. Mobile, Wireless and Server based attacks
6. Authentication, Accounts and vulnerability assessments
7. Business continuity and risk mitigation
8. Legal and ethical factors of courses of action and recovery
Secure Programming issues (14%)
1. Authentication, Authorization, Database Design and Validation, Encryption, Exception Handling & Logging, Framework Security, Session Management, common web and application attacks
  1. .NET, C#, MS-SQL
  2. Java, SQL, other databases
Security Analyst(10%)
1. Range of defenses against attacks
2. Legal, ethical, privacy issues related to computer and information security
3. Improving security over time, responses to security crime and developing scenarios to test possible outcomes.
4. Importance, creation & maintenance of a Disaster Recovery in the enterprise
5. Developing a secure network
6. Security policy and procedures frameworks and implementation
7. Cloud and other virtualization technologies
8. Use and role of traditional and virtual technologies in disaster recovery

10. Data Foundations Examination
1.   Data, Information & Knowledge (3%) D2
2.   Describing, Understanding & Managing Data    (35%)        D4
3.   Data Roles: (4%) D2
4.   Data Models and Relationships (18%)      D3
5.   Data Value and Quality (4%) D3
6.   Data Access, Storage, Protection & Security     (6%) D4
7.   Data Atrophy, Renewal and Removal, Distribution (2%) D3
8.   Data Uses (Historical, legal...) (6%) D2
9.   Data Storage Technologies and purposes:       (4%) D2
10.   Evolving Business and Data Analytics (Tools)(12%) D3
11.   Corporate mergers, data integration (5%) D2
12.   Data Strategy & Planning (4%) D2
13.   Document and Record Management (2%) D3

11.Data and Information Quality Exam

Exam Outline
1.0 Data & Information Quality Concepts, Process & Roles 20%
    1.1 Concepts and Business Drivers (14)
    1.2 Data & Information Quality Organizational Roles (6)
2.0 Data & Information Quality Audit Process 30%
    2.1 Data & Information Quality Assessment (15)
    2.2 Data Profiling Analysis (15)
3.0. Data & Information Quality Remediation 30%
    3.1 Cleanup Methods (15)
    3.2 Process Improvement (15)
4.0 Ongoing Data & Information Quality Activities 20%
    4.1 Data Certification Process (15)
    4.2 Data Governance Framework (5)

12.Data Communications and Internetworking Exam

Exam Outline
1. Data Communications Concepts (12) D4
    1.1 Concepts
    1.2 Protocols
    1.3 Layering
    1.4 Interfaces
    1.5 Wireless, LANs, WANs, MANs
    1.6 Internet Protocol (IP) Telephony
2. Networking Concepts (15) D5
    2.1 Topology
    2.2 Connectivity
    2.3 Queuing theory
    2.4 Flow and capacity
3. The ISO Open Systems Interconnect (OSI) Reference Model (13) D4
    3.1 Physical layer
    3.2 Data link layer
    3.3 Network Layer
    3.4 Transport Layer
    3.5 Session Layer
    3.6 Presentation Layer
    3.7 Application Layer
4. TCP/IP or Internet Reference Model (20) D5
    4.1 Physical Layer
    4.2 Data Link Layer
    4.3 Network / Internet Layer
    4.4 Transport Layer
    4.5 Application Layer
5. Established Communications Systems (15) D5
    5.1 Standards organizations and standards
    5.2 Telecommunications
    5.3 Data Communications
    5.4 Computer Communications and Networks
    5.5 Wireless Technologies
6. Hardware (15) D4
    6.1 Data Switches
    6.2 Modems/Codecs
    6.3 Multiplexors/Concentrators
    6.4 Communications controllers
    6.5 Front-end processors
    6.6 Buses and channels
    6.7 Fiber optical devices
    6.8 Connectors and cables
    6.9 Telephone systems
    6.10 Computer workstations
    6.11 Installation of equipment
    6.12 Diagnostic equipment
    6.13 Wireless equipment
    6.14 Broadband and baseband

13.Data Modeling (Data Development) Exam

Exam Outline
1.0 Data Development Concepts and Technology Types (17) Section 1 Total
    1.1 Concepts (12) D3
    1.2 Tools and Technology Types (5) D3
2.0 Data Analysis and Design (60) Section 2 Total
    2.1 Analyzing Information Requirements (3) D4
    2.2 Data Model / Design Components (57) D4
3.0 Related Data Designs (7) Section 3 Total
    3.1 Information Product Designs (2) D4
    3.2 Data Access and Data Integration Services Designs (5) D4
4.0 Data Model and Design Quality Management (10) Section 3 Total
    4.1 Data Model Design Standards, Versioning and Integration (7) D3
    4.2 Data Model / Database Design Reviews (DBA3.6) (3) D3
5.0 Data Implementation (6) Section 4 Total
    5.1 Database Development and Testing (4) D4
    5.1 Database Deployment (2) D4

14.Data Governance & Stewardship Exam

Exam Outline
1.0  Data Governance Concepts, Business Drivers and Roles (10)
       1.1 Concepts and Definitions (5) D3
       1.2 Business Drivers and Organizational Awareness (5) D3
2.0  Data Governance and Stewardship Roles (15)
       2.1 Data Governance Roles and Organizations (8) D3
       2.2 Data Stewardship Roles (7) D3
3.0  Data Governance Inputs and Deliverables (15)
       3.1  Inputs (8) D3
       3.2  Primary Deliverables (7) D3
4.0  Data Governance Function (30)
       4.1 Data Governance Capabilities Assessment (8) D4
       4.2 Data Governance Function Activities (20) D4
4.3 Data Governance Ethics (2) D3
5.0  Fundamental Knowledge Areas for Data Stewardship Activities (30)
       5.1 Enterprise Data Architecture Management (3) D4
       5.2 Data Development (3) D4
       5.3 Data Operations Management (3) D4
       5.4 Data Security Management (4) D4
       5.5 Meta-data Management (3) D4
       5.6 Reference and Master Data Management (3) D4
       5.7 Data Warehousing and Business Intelligence (3) D4
       5.8 Document and Content Management (3) D4
       5.9 Data Quality Management (3) D4

15.Data Integration and Interoperability (DII) Exam

Exam Outline
1. Data Integration and Interoperability Approaches
    1.1 Data Integration and Interoperability Concepts
    1.2 Data Integration Drivers and Value
2. DII Architectures and Data Patterns
    2.1 DII Architectures
    2.2 DII Data Patterns
3. DII Processes, Technologies, and Standards
    3.1 Techniques and Technologies
    3.2 DII Standards
4. Data Integration Systems
    4.1 System Types
    4.2 Data Integration Systems Development Life Cycle (SDLC)
5. Organizational Roles and DII Governance
    5.1 Organizational DII Roles and Practices
    5.2 DII Governance

16.Data Management Exam

Exam Outline
1.0. Data Management Process (5) Section 1 Total
    1.1 Concepts (3) D3
    1.2 Data Roles (2) D3
2.0 Data Governance Function (10 ) Section 2 Total
    2.1 Data Management Planning (4) D4
    2.2 Data Management Control (6) D4
3.0 Data Architecture Management Function (12) Section 3 Total
    3.1 Architecture Overview (6) D4
    3.2 Enterprise Architecture Types & Techniques (6) D4
4.0 Data Development Function (12) Section 4 Total
    4.1 Analyzing Data / Information Requirements (4) D4
    4.2 Data Model Component Management and Data Implementation (8) D4
5.0 Data Operations Management Function (8) Section 5 Total
    5.1 Database Support (4) D3
    5.2 Data Technology Management (4) D3
6.0 Data Security Management Function (8) Section 6 Total
    6.1 Data Security Principles (4) D4
    6.2 Data Security Implementation (4) D4
7.0 Reference and Master Data Management Function (8) Section 7 Total
    7.1 Reference Data (4) D3
    7.2 Master Data Management (4) D3
8.0 Data Warehousing and Business Intelligence Management Function (12) Section 8 Total
    8.1 Data Warehousing (8) D4
    8.2 Business Intelligence (4) D3
9.0 Document and Content Management Function (5) Section 9 Total
    9.1 Document / Records Management (2) D3
    9.2 Content Management (3) D3
10.0 Meta-data Management Function (5) Section 10 Total
    10.1 Meta-data Concepts (2) D4
    10.2 Meta-data Management Implementation (3) D4
11.0 Data Quality Management Function (15) Section 11 Total
    11.1 Data & Information Quality Principles (5) D3
    11.2 Data & Information Quality Profiling / Assessment / Audit (5) D4
    11.3 Data & Information Quality Improvement (5) D3

17.Data Base Administration (Data Operations) Exam

Exam Outline
1.0. Data Operations Management (56) Section 1 Total
1.0. Data Operations Management (56) Section 1 Total
    1.1 Database Support (50) D4
    1.2 Database Standards (6) D5
2.0. SQL Considerations (14) Section 5 Total
    2.1 SQL Basics (2) D4
    2.2. DDL - Data Definition Language (2) D4
    2.3. DML - Data Manipulation Language (6) D5
    2.4. DCL - Data Control Language (2) D5
    2.5. Data Dictionary (Systables) (2) D5
3.0 Data Technology Management (15) Section 3 Total
    3.1 Planning, Evaluation and Selection (9) D3
    3.1 Implementing Data Technology (6) D3
4.0 Data Security Management (15) Section 3 Total
    4.1 Data Security Principles (8) D3
    4.2 Data Security Implementation (7) D31.1 Database Support (50) D4

18. Data Science
The Data Science specialty examination involves, collecting and cleansing of data and building new analysis of that data, often simultaneously, to exploit new information about that data and repeating steps after initial understanding and learnings emerge.

1. Business & Technology Issues: Starting with the Question first -What problem are you trying to solve? (16%)
1.1. Business
1.1.1. Data governance
1.1.2. Data security
1.1.3. Data strategy
1.1.4. Change management
1.1.5. Trends and directions in the industry
1.2. Technical/Scientific
2. Data Storage, Big Data and Sources (25%)
2.1. Geo-position data
2.2. Devices data: Internet of Things and Sensor Data
2.3. Social media data:
2.4. Primary data and Published research
2.5. Health research and data,
2.6. Open public data
2.7. Technologies: Hadoop, RDBMS, NoSQL
2.8. Cloud and agile
2.9. Structured and unstructured data
2.10. Databases, Data Marts, Data Lakes
3. Mathematics and Statistical Data Science (13%)
Mathematics and Statistics focuses on the science of learning from data.
3.1. Uncertainty and the role of statistics
3.2. Algorithms and their development
3.3. Data Science Programming Languages
3.3.1. R language
3.3.2. Python
3.3.3. Others – Java, Perl, SAS, SPSS, SQL
3.4. Data science uses
3.5. Data Science techniques (small sample provided)
3.5.1. Regression
3.5.2. Estimation, Intervals
3.5.3. Hypothesis testing
3.5.4. Pattern recognition, supervised learning
3.5.5. Clustering, Segmentation
3.5.6. Time Series, Decision Trees, Random numbers, Monte Carlo
3.5.7. Bayesian statistics; naïve Bayes
3.5.8. Association rules
3.5.9. Nearest neighbour
3.5.10. Model fitting
3.5.11. Predictive modeling
4. Programming Skills (3%)
4.1. Computer programming with R, including JSON
4.2. Substantive Expertise/ Experience
5. The Data Analytic Question and Types of Data & Reporting (4%)
5.1. Summary data
5.2. Descriptive data
5.3. Exploratory data
5.4. How Data are affected
5.4.1. Causal
5.4.2. Mechanistic: Deterministic
5.4.3. Inferential
5.4.4. Predictive
5.4.5. Common mistakes
5.4.5.1. Correlation versus causation
5.4.5.2. Model Building and Testing
5.4.5.3. Small sample size (n) inferences
5.4.5.4. Data dredging
6. Tidying the data – Data cleaning and quality (6%)
6.1. Components of a data set
6.1.1. Raw data
6.1.2. A tidy data set
6.1.3. Metadata
6.1.4. Documented process
6.1.5. Published research
6.2. Common mistakes
6.3. Checking the data
6.3.1. Quirks and potential errors
6.3.2. Coding variables
6.3.2.1. Methods
6.3.2.2. Errors
6.3.2.3. Common mistakes
7. Exploratory analysis (6%)
7.1. Summarizing and visualizing data prior to analysis
7.2. Interactive analysis
7.3. Common Mistakes
8. Statistical Modeling and Inference (7%)
8.1. Best estimate
8.2. Level of uncertainty
8.3. Size
8.4. Exploratory and confirmatory analysis
8.5. Defining population, sample, individuals and data
8.6. Unrepresentative sample, confounders, distribution of missing data, outliers,
8.7. Small or very large samples
8.8. Multiple hypothesis tests and correcting for multiple tests
8.9. Smoothing data over space and time
8.9.1. Regression
8.9.2. Locally weighted scatterplot smoothing
8.9.3. Smoothing splines, moving averages, Loess
8.10. Real sample size
8.11. Common mistakes
8.11.1. Dependencies
8.11.2. p-values versus confidence intervals
8.11.3. Inference with exploration
8.11.4. Assumptions about models and fit
8.11.5. Conclusions about populations
8.11.6. Uncertainty
9. Prediction and Machine Learning (8%)
Creation of training sets from sample data and some variables become features, others become outcomes with the goal to build an algorithm or prediction function taking a new set of data from an individual data set and best guess (estimate) the outcome value.
9.1. Splitting data into training and validation sets
9.2. More data versus better algorithms
9.3. Features versus algorithm
9.4. Definition of error and measure
9.5. Overfitting and validation
9.6. Prediction accuracy and multiple models
9.7. Prediction trade-offs
10. Causality (identifying average affects between noisy variables) (2%)
10.1. Causal data and non-randomized experiments
10.2. Difficulties associated with interpreting cause
10.3. Confirming randomization worked
10.4. Avoiding causal language or techniques
10.5. Common mistake(s)
11. Written Analysis (9%)
11.1. Communicating the message
11.1.1. Question
11.1.2. Writing for non-technical audiences
11.1.3. Text, figures, equations, code (algorithms) and how they contribute to or detract from the story
11.2. Components
11.2.1. Title, Introduction or motivation for the study, including question
11.2.2. Experimental design – source of data, collection methods, relevant technologies and methods used to collect the data.
11.2.3. Data set, tidy data (clean data), sample sizes, number of variables, averages, variances and standard deviations for each variable
11.2.4. Description of the statistics or machine learning models used
I. Precise mathematical model(s)
II. Specification of terms
11.2.5. Results including measures of uncertainty
I. Assumptions
II. Errors
III. Independent or dependent and/or common variance
IV. Sampling and use of different dispersions (heteroskedastic)
11.2.6. Parameters of interest
11.2.6.1. Estimates, scale of interest; Estimate: Why, how and meaning
11.2.6.2. Measures of uncertainty: Standard deviations, confidence intervals, credible intervals
11.2.7. Conclusions and potential problems including with the study
11.2.7.1. Problems: outliers, missing data
11.2.7.2. Exclusions: exploratory analysis, test development – do they contribute to the story or not?
11.2.8. Data Visualizations
11.2.8.1. Graphs, charts, figures, histograms
11.2.8.2. Documentation and Clarification: Colour, Size, Labeling (units of measure), Legends
11.2.8.3. Visualization errors: displaying data badly
11.2.9. References
12. Reproducibility (1%)
12.1. Reproduction of results by third parties for verification
12.2. Data, Figures, R Code, Text
12.2.1. Data – raw, processed, set seed (bootstrap or permutations)
12.2.2. Figures – exploratory, final
12.2.3. R Code – raw, unused scripts; data processing scripts; analysis scripts
12.2.4. Text – Readme Files; Final data analysis products – presentations, reports.
12.3. Literate programming and version control
12.3.1. R markdown
12.3.2. Python notebook

19. Data Warehousing Exam

The Data Warehousing specialty examination is designed to test the candidate's knowledge of the theory and practice of data warehousing from the warehouse infrastructure creation / maintenance, analysis / design, data acquisition / cleansing to implementation / operation. It also tests the knowledge of theory and practice of the data warehousing function, organizational skills required, and roles and responsibilities of the data warehousing professional within the enterprise.

Exam Outline
1.0 Data Warehousing Management (16) Section 1 Total
    1.1 Data Warehousing Project (4) D3
    1.2 Data Warehousing Program (8) D3
    1.3 Data Warehousing Roles (4) D3
2.0 Data Warehousing Infrastructure Architectures (11) Section 3 Total
    2.1 Enterprise Architectures for Warehouse Planning (2) D4
    2.2 Data Warehousing Architectures (9) D4
3.0. Tools and Technology Types (13) Section 5 Total
    3.1. Data Warehousing (8) D3
    3.2 Business Intelligence (5) D3
4.0 Data Warehousing Analysis (13) Section 6 Total
    4.1 Requirements Analysis (9) D5
    4.2 Source Data Analysis (4) D4
5.0 Warehousing Data Modeling and Database Design (18) Section 7 Total
    5.1 Model Types and Components (9) D5
    5.2 Data Modeling for the Data Warehouse (9) D5
6.0 Data Integration (13) Section 7 Total
    6.1 ETL System Functionality (5) D4
    6.2 ETL and Alternative Data Integration Development (8) D4
7.0 Other Warehousing Development Activities (6) Section 8 Total
    7.1 Warehousing Applications and Databases (4) D3
    7.2 Warehousing Training and Documentation (2) D3
8.0 Implementation and Ongoing Activities (10) Section 9 Total
    8.1 Deployment (5) D3
    8.2 Ongoing Support and Maintenance (5) D3

19.Project Management for IT Exam

The IPM-IT is designed to test the candidates' knowledge of Integrated IT Project Management theory and practices at a level of competency appropriate to senior IT project management professional. This examination contains questions that are specific to the Integrated IT Project Management (IPM-IT) methodology, based on PMI-PMBOK standards, and Rational Unified Process (RUP) software development methodology.

Exam Outline
1. Integrated IT Project Management (IPM-IT) FRAMEWORK
    1.1 Business Management
    1.2 Project Management
    1.3 Information Technology (IT) Management
2. Business Management Model
    2.1 Program Business Systems Architecture
    2.2 Project value Justification
    2.3 Project Funding Allocations
    2.4 Project Deliverables/Funding Approvals
    2.5 Program Steering and Working Committee
    2.6 Business Initiatives Support
3. Project Management Model
    3.1 IT Project Delivery Life Cycle
    3.2 IT Project Management Delivery Processes
    3.3 IT Project Management Office (PMO) Processes
4. Information Technology (IT) Management Model
    4.1 Cost Estimating
    4.2 Resource Allocations
    4.3 Data Architecture
    4.4 Applications Architecture
    4.5 Technology Architecture
    4.6 Applications Support Services
5. Integrated IT Project Delivery Life Cycle Model
    5.1 Definition Phase
    5.2 Requirements Analysis Phase
    5.3 Architecture Phase
    5.4 Iterative Development Phases
6. Aligning PMBOK Processes with RUP
    6.1 Inception Phase
    6.2 Elaboration Phase
    6.3 Construction Phase
    6.4 Transition Phase

20.Information Systems Management Exam

The Management exam is designed to test the candidate's knowledge of the theory and practice of management. Its emphasis is on the practices and standards required of senior IS professionals engaged in the management and administration of systems-related activities.

Exam Outline
1. Management and Information Systems Decision Concepts
     1.1 Management Functions and IS Business Drivers
     1.2 IS Decisions and Skills Required
2. Strategies and Business Process Management (BPM)
     2.1 IS Influences on Strategies
     2.2 IS Influences on Strategies
3. Architecture and Infrastructure
     3.1 Enterprise Architecture
     3.2 IT Architecture
4. IS / IT Sourcing and Funding
     4.1 IS / IT Sourcing
     4.2 IS / IT Funding
5. IS / IT Organization, Governance and Ethics
     5.1 IS Organization
     5.2 IS / IT Governance, Ethics and Privacy
6. Project Management
     6.1 Project and Its Management
     6.2 Project Methodologies and Techniques
7. Managing Data, Information, and Knowledge, and Using Business Analytics
     7.1 Data, Information and Knowledge
     7.2 Business Intelligence and Business Analytics

22. IT Consulting Exam

Exam Outline
1.0 Consulting Function (25%)
    1.1 Planning (5)
    1.2 Business Operations (10)
    1.3 Consulting Skills (10)
2.0 Consulting Work Acquisition (15%)
    2.1 Marketing (5)
    2.2 Sales (4)
    2.3 Proposals (3)
    2.4 Contracts (3)
3.0 Consulting work management (15%)
    3.1 Project management (7)
    3.2 Time management (6)
    3.3 Client Roles (2)
4.0 Client Management (20%)
    4.1 Organizational awareness (6)
    4.2 Maintaining client relationships (6)
    4.3 Client’s Business Environment (5)
    4.4 Client Education (3)
5.0 Ethical Guidelines and Professional Standards (25%)
    5.1 Ethical Issues (20)
    5.2 Guidelines and Standards (5)

23. Microcomputers and Networks Exam

This Microcomputing and Networks exam is intended to test the candidate's knowledge of the theory and professional practices associated with the management of workgroup clusters of microcomputing devices at the conceptual, logical, and physical levels.

Exam Outline
1. Resource Management Functions
    1.1 General Administration
    1.2 Technical Administration
    1.3 End User Support
2. Microcomputer Architecture
    2.1 System Unit
    2.2 Peripherals
3. Microcomputer Software
    3.1 Applications
    3.2 Systems Software
4. Network Technology
    4.1 Networking Concepts
    4.2 Local Area Networking (LAN)
    4.3 Wide Area Networking (WAN)
    4.4 Value Added Networks (VAN)

24. Object Oriented Analysis and Design Exam

As applications development improves software production technologies, object oriented methods require changes in analysis and software design. This examination establishes the standards required for knowledge and experience in this area.

Exam Outline
1. Object Theory (10%)
    1.1 Definition of objects
    1.2 Objects in the data world
    1.3 Methods
2. Models and Modeling (10%)
    2.1 Designing the Model
    2.2 Assigning Object Responsibilities
    2.3 Designing The Classes
    2.4 Building Applications
    2.5 Extending the System
3.   Objects and Classes (25%)
    3.1 Abstraction & encapsulation
    3.2 Composition
    3.3 Inheritance
    3.4 Classification
    3.5 Polymorphism
    3.6 Overloading
4. Object Models (25%)
    4.1 Definition and background
    4.2 Essential Elements
    4.3 Design Considerations
    4.4 Advantages and disadvantages of using object models
5. Development Methodologies - historical perspective (5%)
6. OODLC (25%)
    6.1 Analysis
    6.2 Design
    6.4 Testing
    6.5. Maintenance
    6.6 Security & disaster planning

25. Office Information Systems Exam

The Office Information Systems exam is intended to test the defined body of knowledge and professional practices associated with the management of today's modern office.

Exam Outline
1. Office Environment
    1.1 Centralization/Decentralization - Issues for Work Groups and Systems
    1.2 Environmental Engineering for Efficiency
    1.3 Technology Evaluation
2. Office Technologies
    2.1 Internal/External Communications
    2.2 Image
    2.3 Storage Media
    2.4 Public Access Technologies
    2.5 Installation, Maintenance and Security of Information Systems
    2.6 Records Management
    2.7 Managing to Prevent Obsolescence
3. End-User Computing
    3.1 Product Evaluation, Analysis and Support
    3.2 Information Center
    3.3 Coordinating and Supporting End-User Application Development
    3.4 Managing Resistance

26. Procedural (Advanced) Programming Exam

The Procedural (Advanced) Programming exam establishes part of the recognized professional standards for senior-level software developers.

Exam Outline
1. Data and File Organization
    1.1 Data Formats, Internal and External
    1.2 Data Structures
    1.3 File Structures
    1.4 Database Models
2. Program Design
    2.1 Process
    2.2 Methods
    2.3 Representation
3. Procedural Programming Structure
    3.1 Data Definition
    3.2 Control Structures
    3.3 Subprograms
4. Procedural Programming Considerations
    4.1 Order of Implementation
    4.2 Exception and Interrupt Handling
    4.3 Style
    4.4 Program Efficiency
    4.5 Testing and Debugging
    4.6 Maintenance Procedures
    4.7 Fundamental Algorithms
5. Integration with Hardware and Software
    5.1 Hardware Components
    5.2 Language Paradigm Selection
    5.3 Utilities
    5.4 Operating Systems Interface
    5.5 Communications and Distributed Processing

26a. Public Sector Data Governance Exam
The Public Sector Data Governance exam addresses the needs of public sector organizations in governing data according to various federal regulations. In the USA these are specific presidential orders and legislation approved by the congress and senate.
1.0 Data Governance Concepts and Mission Drivers (15%)
1.1 Concepts & Definitions
1.1.1 Definitions
• Data Governance/Information Governance
• Data Stewardship
• Data Ownership
• Data Curation
• Relationships between owner/steward/curator roles
1.1.2 Key Public Sector Concepts
• Data Lifecycle
• Open Data
• Right to Information
• Restricted data
1.2 Governance Organizational Structures
1.2.1 Data Governance Council
• Typical membership of a public sector data governance council
• Role of the Chief Data Officer in a public sector organization
1.2.2 Centralized vs. Federated Governance Models
• Use of Federated Governance in a public sector context
• Open Archival Information System
1.3 Mission Drivers
1.3.1 Data as a democratizer
• Improving Discovery
• Enabling Reuse
1.3.2 Data as enabler of good government
• Using data to increase government accountability
• Using data to improve public welfare
1.3.3 Data as a national security issue
• Cybersecurity issues in public sector governance
• National security exceptions to standard governance protocols
1.4 Data Governance & Stewardship Management Tools
1.4.1 Meta-data tools and repositories
• Establish metadata standards first
• Tools apply standards, but do not create them
• Examples of relevant metadata standards applied by common tools (Dublin Core, ISO 19115, Darwin Core)
1.4.2 Data asset inventory
• Definition and process
• Utility in risk management
• Utility in identifying data gaps
• Utility in increasing data management maturity
1.4.3 Data modeling tools
• Conceptual vs. logical vs. physical data models
• COTS vs. Internal development
1.4.4 Communication tool suite
• Internal email and intranet
• Blogging, podcasts, wikis
• Collaborative workspaces
2.0 Legal and Regulatory Environment (20%)
2.1 Federal data governance statutes
2.1.1 Freedom of Information Act
• Right to request access to records from any federal agency
• Nine exemptions protecting personal privacy, national security, and law enforcement
• State and local government parallel FOIA, FOIP laws and regulations
2.1.2 US Privacy Act
• What is a system of record?
• Collection, maintenance, use, and dissemination of information about individuals that is maintained in systems of records by federal agencies
2.1.3 Health Insurance Portability and Affordability Act
• Privacy Rules
• Covered Entities
2.1.4 Federal Records Act
• Preservation Requirement
• Organization, functions, policies, decisions, procedures, and essential transactions
2.1.5 Federal Information Security Modernization Act
• DHS authority to administer the implementation of information security policies for non-national security federal Executive Branch systems
• Office of Management and Budget's (OMB) oversight authority over federal agency information security practices
• State, local, and tribal government requirements under FISMA
2.2 Regulatory frameworks
2.2.1 Executive Order 13556 on Controlled Unclassified Information
• Executive Agent
• Categories and subcategories of control markings
2.2.2 Executive Order 13526 on National Security Information
• Original and Derivative Classification Authority
• Requirements for security markings on data
• Rights and responsibilities of classifying agencies
2.2.3 2013 Open Data Policy
• Open Data Provisioning at Municipal, State/Provincial, Federal/National levels (Decision authorities and releasing of appropriate data)
• Management of information as an asset
• Promotion of the openness and interoperability of government data and information
2.2.4 Commerce Department Privacy Shield (Global universal data privacy regulations)
• Resulting from European Union data privacy laws
• Compliance mechanism for transferring personal data from the European Union and Switzerland to the United States in support of transatlantic commerce
• Self certification provisions (2017-not sufficient under EU Court)
2.3 Data Sharing & Ethics
• Responsibilities of the government to the governed - right of access to personal information
• Reasonable use of data in a public sector environment
• Risks of both oversharing and of lack of sharing
• Personally identifiable information in a public sector context
• Specific national security responsibilities and exemptions
3.0 Data Governance and Stewardship Roles & Responsibilities (20%)
3.1 Data Governance Roles
3.1.1 The Role of Chief Data Officer (CDO)
• Responsibilities differentiated from the CIO
• Relationship to organizational leadership
• CDO change management strategies
• Assessing Data Governance/Organizational Maturity
3.1.2 Composition of a Public Sector Data Governance Council
• CDO
• Data Owners or their proxies
• Information Management Executives
• IT Senior Management
• Data User Representatives
3.1.3 Data Owner
• Public sector ownership of data
• Decision Rights
• Responsibilities for sharing and discovery
3.1.4 Data Steward
• Interagency stewardship responsibilities
• Business Data Steward
• Technical Data Steward
• Coordinating Data Steward
• Information Management Officer
3.1.5 Data Custodian
• Bridge between the consuming IT system and data stewardship
• Possible overlaps with technical data stewardship responsibilities
3.1.6 Data Curator
• Responsibility to enable data discovery, add value, provide for reuse over time
• Creation of documentation and contextual metadata standards
• Enablement of data analytics through effective curation
3.2 Public Sector Stakeholder Management
3.2.1 Data Providers
• Negotiation of data licenses in a public sector environment
• Responsibility to avoid duplication of data to reduce costs to taxpayers
3.2.2 Data Users
• Understanding the data needs and ultimate goals of governmental knowledge workers
• Understanding the needs of the public in terms of access to data
• Managing expectations of data users in a restricted data environment
3.2.3 Education and Training of Stakeholders
4.0 Data Governance Inputs and Deliverables (20%)
4.1 Inputs
• Mission goals
• Data Collection Authorities
• Data Protection and Use Requirements
• Reporting Requirements
• Funding parameters and constraints in a public sector environment
4.2 Primary Deliverables
4.2.1 Data Governance Policies
• Accountability and ownership policies
• Public Sector governance best practices
• Access Control Policies
• Data Discoverability (Rules relating to access to data, that does not allow sharing across agencies, without justification (legal positions - vis-a-vis State, Department rules)
• Data Retention Schedules
• Disaster Recovery Plans
4.2.2 Data Governance Standards and Procedures
• Metadata Standards
• Documentation Standards
• Data Lineage
• Dissemination and Sharing Procedures
• Clearance and access requests
• Systems of Record identification and maintenance
4.2.3 Data Governance Goals and Outputs
• Increase Knowledge of Data Governance through training and education
• Change management
• Increased Use of Data for Public Good
• Decisions
5.0 Fundamental Knowledge Areas for Public Sector Data Stewardship (25%)
5.1 Data Preservation Planning
• Role of the National Archives
• Planning for legacy data formats and file types
• Need for adequate funding for data and records preservation
5.2 Data Access and Security Issues
5.2.1 Concepts and Terms
• Security classification
• Access control
• Use of role-based access
• Data leakage
• Malicious attack
• Privacy
5.3 Reference and Master Data Management
5.3.1 Key Concepts
• The “golden record”
• Difference between master data and metadata
• Scoping “master data” in public sector environments
5.3.2 MDM tools and methodologies
• Tools help you consolidate and view your master data, but won’t create it for you
• How to evaluate tools for public sector MDM
5.4 Metadata Management
5.4.1 Concepts and Terms
• Meta-data types (technical, ownership, contextual)
• Meta-data perspectives (lineage, definitions, rules)
• Role of data curation in effective metadata management
5.4.2 Stewardship Activities
• Data definitions
• Data cataloging
• Data lineage
• Data sharing agreements
5.5 Document and Content Management
5.5.1 Key Concepts
• Overlap between data management and information management
• Records Management
• Unstructured and semi-structured content
5.6 Data Warehousing and Cloud Solutions
5.6.1 Concepts and Terms
• Data warehousing
• Cloud Computing
• ETL
• Data mart
5.6.2 Stewardship Activities
• Defining requirements
• Establishing authoritative data sources
• Metrics definition
• Data ownership in a warehouse environment
5.6.3 Risk/Reward Calculations
• Enabling information sharing across jurisdictional boundaries
• Privacy issues with cloud solutions
5.7 Data Quality Management
5.7.1 Concepts and Terms
• Quality definition in public sector context
• Data quality principles
5.7.2 Activities
• Identifying data quality issues
• Data quality assessment
• Data profiling and auditing
• Data cleansing

27. Software Engineering Exam

The Software Engineering exam addresses all of the issues of software delivery as approached from the discipline of software engineering.

Exam Outline
1. Computer System Engineering
    1.1 Computer-Based Systems
    1.2 Computer-System Life Cycle Modeling
  1.3 Hardware Considerations
    1.4 Software Considerations
    1.5 Human Considerations
2. Software Project Planning
    2.1 Project Planning Objectives
    2.2 Software Scope
    2.3 Resources
    2.4 Metrics for Software Productivity and Quality
    2.5 Software Project Estimation
    2.6 Decomposition Techniques
    2.7 Empirical Estimation Models
    2.8 Automated Estimation Tools
    2.9 Software Project Scheduling
    2.10 Software Acquisition
    2.11 Organizational Planning
    2.12 The Software Project Plan
3. Software Requirements
    3.1 Analysis Principles
    3.2 Object-Oriented Analysis
    3.3 Software Prototyping
    3.4 Systems Analysis
    3.5 Requirements Analysis Methodologies
    3.6 Data Flow-Oriented Analysis Methods
    3.7 Data Structure-Oriented Methods
    3.8 Data Structured Systems Development
    3.9 Jackson System Development
    3.10 Automated Tools for Requirements Analysis
4. Software Design
    4.1 The Design Process
    4.2 Design Fundamentals
    4.3 Modular Design
    4.4 Data Flow-Oriented Design
    4.5 Data Structure-Oriented Design
    4.6 Object-Oriented Design
    4.7 Real Time Design
    4.8 Model-Based Design
    4.9 Procedural Design
    4.10 Design Documentation
5. Programming Languages and Coding
    5.1 The Translation Process
    5.2 Programming Language Characteristics
    5.3 Programming Language Fundamentals
    5.4 Language Classes
    5.5 Programming Aids
    5.6 Coding Style
    5.7 Efficiency
6. Software Quality Assurance
    6.1 Software Quality and Quality Assurance
    6.2 Software Reviews
    6.3 Formal Technical Reviews
    6.4 Software Quality Metrics
    6.5 Software Reliability
    6.6 Software Quality Assurance Approach
7. Software Testing Techniques
    7.1 Software Testing Fundamentals
    7.2 White Box Testing
    7.3 Basis Path Testing
    7.4 Loop Testing
    7.5 Black Box Testing
    7.6 Proof of Correctness
    7.7 Automated Testing Tools
    7.8 Strategic Approach to Software Testing
    7.9 Unit Testing
  7.10 Integration Testing
    7.11 Validation Testing
    7.12 System Testing
    7.13 Debugging
8. Software Maintenance and Configuration Management
    8.1 Maintenance Characteristics
    8.2 Maintainability
    8.3 Maintenance Tasks
    8.4 Maintenance Side Effects
    8.5 Software Configuration Management

28. Systems (Operating Systems) Programming Exam

The Systems Programming specialization is concerned with providing higher-level, shared access to computing resources via applications-independent mechanisms.

Exam Outline
1. Languages
    1.1 Assembly Language Concepts
    1.2 Higher Level Language Structures
2. Operating Systems
    2.1 Processor Dispatching
    2.2 Interrupt Handling
    2.3 Paging Supervisor
    2.4 Resource Allocation
    2.5 Input/Output Spooling
    2.6 Operator Communication
    2.7 Program Loading
    2.8 Memory Protection and Privileged Instructions
3. Language Processing
    3.1 Parsing and Syntactic/Semantic Analysis
    3.2 Code Generation and Optimization
    3.3 Module Collection and Address Resolution
    3.4 Development Techniques
4. Concurrent and Distributed Processing
    4.1 Communication Protocols
    4.2 Network Architecture
    4.3 Multi-Tasking
    4.4 Dynamic Resource Allocation
    4.5 Fault-Tolerance and Recovery
    4.6 Security
5. Data Management Systems
    5.1 Physical Data Structure
    5.2 Logical Data Models
    5.3 Concurrent Access Control
    5.4 Data Integrity
6. Computer Architecture and Implementation
7. Performance Evaluation
    7.1 Performance Measurement
    7.2 Modeling and Simulation
    7.3 Tuning
8. Software Tools
9. System Management
    9.1 Security
    9.2 Software Installation
    9.3 Software Tailoring

29. Systems Development Exam

The Systems Development exam is designed to test the candidate's knowledge of the theory and practice of systems analysis, systems design, and systems implementation. It also tests the role of the systems professional within the enterprise.
Exam Outline
1. Systems Analysis
    1.1 General System Theory
    1.2 Preliminary Studies
    1.3 Definition of Objectives
    1.4 Data Gathering and Analysis
    1.5 System Requirements
2. Systems Design and Implementation
    2.1 Alternative Systems Design
    2.2 Logical Design
    2.3 Detailed Design
    2.4 Privacy, Security and Controls
    2.5 System Implementation
    2.6 System Evaluation and Maintenance
3. The Systems Analyst as a Professional
    3.1 Organizational Roles of the Systems Professional
    3.2 Interpersonal Roles of the Systems Professional
    3.3 Communication Skills
    3.4 Identifying Key Individuals

30. Systems Security Exam

The Systems Security exam is intended to test the candidate's knowledge of the theory and professional practices associated with the development and management of Systems Security programs.

Exam Outline
1. Risk Assessment
    1.1 Organization
    1.2 Systems and Data Asset Valuation
    1.3 Threat Characteristics
    1.4 Risk Assessment
    1.5 Dealing with Risk
2. Recovery from Information Service Interruptions
    2.1 Recoverable Storage Management
    2.2 Business Continuity Planning
    2.3 Disaster Management
3. Information and System Security
    3.1 Telecommunications
    3.2 Database Security
    3.3 Cryptography
    3.4 Operating Systems
    3.5 Microcomputers and Local Area Networks
    3.6 Physical Security
4. Security in System Design
    4.1 System Security Objectives and Functions
    4.2 Data Integrity Assurance
    4.3 Life Cycle Approach
5. Security Management
    5.1 Policy Setting, Implementation and Administration
    5.2 Security Awareness
    5.3 Information Ethics
    5.4 Personnel Issues
    5.5 Evaluation of Security Measures

31. Web Development Exam

Exam Outline
1. General Page Design Concepts
    1.1 Wireframes
    1.2. Cascading Style Sheets (CSS)
    1.3. Color Theory
    1.4 Aspect ratio for screens
    1.5 Display resolution
    1.6 Bgcolor
    1.7. Background image
    1.8. Accessibility
2. General Server Concepts
    2.1. Clustering
    2.2 HTTP Server Software
    2.3. Front Page Server Extensions
    2.4 Common Gateway Interface (CGI)
    2.5 Content Management System
    2.6 Application Service Provider (ASP)
3. Development Tools/Environments
    3.1. Browser
    3.2 Development Environments/Tools
4. Programming Languages
    4.1 HyperText Markup Language (HTML)
    4.2 Dynamic HTML (DHTML)
    4.3 Extensible Markup Language (XML)
    4.4 Extensible Style Sheet Language (XSL)
    4.5 Languages: server-side, client-side
    4.6. Web Service Description Language (WSDL)
    4.7 PHP: Hypertext Preprocessor
    4.8 Cold Fusion Markup Language (CFML) / .CFM files
5. Programming Concepts
    5.1. Server-side script
    5.2 Client-side script
    5.3 Round-trips
   5.4 Form validation
    5.5 Query strings
    5.6 Object-oriented programming (OOP)
    5.7 Coupling
    5.8 Cohesion
    5.9 Parameters, Arguments
    5.10. Modular design
    5.11 Information hiding
    5.12 Three-tiered/layered model
    5.13 Web Services
    5.14 Simple Object Access Protocol (SOAP)
    5.15 Java 2 Enterprise Edition (J2EE)
    5.16 Active Server Pages (ASP)
    5.17 Distributed Object Models
6. Project Management
    6.1 Life cycle approaches
    6.2 Versioning
    6.3 Source Code Control System
    6.4 Software Quality Assurance
7. Site Management and Security
    7.1 Demilitarized zone
    7.2 Firewall
    7.3 Proxy Server
    7.4 Authentication
    7.5 Copyright
    7.8 Statistics and reporting
8. Database and Data Access
    8.1. Common Packages
    8.2 Relational Model
    8.3 Normalization
    8.4 Data access technologies
    8.5 SQL
    8.6 Stored Procedures
9. Internet and Communications Concepts
    9.1 TCP/IP
    9.2 Uniform Resource Locator (URL)
    9.3 World Wide Web Consortium (W3C)
    9.4 Request For Comments (RFC)
    9.5 Universal Description, Discovery and Integration (UDDI)
    9.6 HyperText Transfer Protocol (HTTP)
    9.7 HTTP Secure (HTTPS)
    9.8 Wireless Application Protocol (WAP)
  9.9 File Transfer Protocol (FTP)
    9.10 Certificate Authority
    9.11 Registrar
    9.12 Domain Name System
    9.13 Virtual Private Network (VPN)
    9.14 Intranet
    9.15 Internet Service Provider (ISP)
    9.16 Wide Area Network (WAN)
    9.17 Local Area Network (LAN)
    9.18 Bandwidth and connection speed
10. File Formats Related to Web Development
    10.1 Streaming media
    10.2 Non-streaming media
    10.3 Additional formats

32. Zachman Enterprise Architecture Exam

Exam Outline
1.0 Zachman Enterprise Architecture Framework Concepts and Roles Total items (15)
    1.1 Zachman Framework Concepts (13)
    1.2 Zachman Framework Organizational Roles (2)
2.0 Planning for Zachman Enterprise Architecture Framework Total items (18)
    2.1 Initial Enterprise Architecture Effort (10)
    2.2 Ongoing Enterprise Architecture Programs / Initiatives (8)
3.0 Zachman Framework Models Principles Total items (47)
    3.1. Enterprise Architecture Fundamentals (10)
    3.2 Zachman Framework Models (20)
    3.3 Zachman Framework Perspectives (17)
4.0 Zachman Framework Infrastructure Management Total items (20)
    4.1. Standards and Rules (15)
    4.2 Model Management (5)

Programming Language Exams
1.C Language Exam

Exam Outline
1. Data Types
2. Operators and Expressions
3. Control Flow
4. Functions
5. Pointers and Arrays
6. Structures and Unions
7. Standard I/O Library
8. Library Functions and Environment
9. The Preprocessor

2.C++ Language Exam

Exam Outline
1. Basic Language Elements
2. Expressions and Operators
3. Flow Control
4. Arrays and Pointers
5. Object Oriented Programming
6. Functions
7. Exception Handling
8. Standard Libraries
9. The Preprocessor

3.COBOL Language Exam

Exam Outline
1. General
2. Data Description
3. Data Manipulation
4. Input/Output
5. Flow of Control
6. Other Language Features

4.Java Language Exam

Exam Outline
1. Java Technology
        1.1 Fundamental concepts Java Programming Language
        1.2 Key Java technology groups
2. Analyzing Problems and Designing Solutions
        2.1 Object Oriented Analysis and Design
        2.2 Designing Object Classes
3. Developing and Testing Java Technology
        3.1 Four components of a class in Java
        3.2 Testing classess using the main method in Java
        3.3 Compiling and executing Java programs
4. Declaring, Intializing, and using Variables
        4.1 Identify, define and use variables in Java
        4.2 Primitive data types
        4.3 Declarations, initialization, and use of variables and constants.
        4.4 Modify values in variables using operators
        4.5 Promotion and type casting
5. Creating and using Objects
        5.1 Declare, instantiate and intialize object reference variables
        5.2 Use String class as specified in the JDK
        5.3 Use Java 2 Platform, Standard Edition (J2SE) class library
6. Operators and Decision Constructs
        6.1 Relational and conditional operators
        6.2 IF and IF ElSE constructs
        6.3 Switch construct
        6.4 While and Do/While Loops
7. Methods
        7.1 Advantages of methods: worker and calling methods
        7.2 Declaring and invoking methods
        7.3 Object and static methods
        7.4 Overloading methods
8. Encapsulation and Constructors
        8.1 Encapsulating to protect data
        8.2 Creating constructors to initialize Objects
9. Arrays
        9.1 One-dimensional arrays
        9.2 Setting array values using length and a loop
        9.3 Pass arguments to main method for use in a program
        9.4 Two-dimensional and multi-dimensional arrays
        9.5 Copying array values from one array to another
10. Inheritance
        10.1 Defining and using inheritance
        10.2 Abstraction
        10.3 Class libraries and use in code
11. Object-oriented Programming
        11.1 Modeling concepts: abstraction, encapsulation and packages
        11.2 Code reuse in Java
        11.3 Class, member, attribute, method, constructor, and package
        11.4 Access modifiers: Public and Private
        11.5 static variables, methods, and initializers
        11.6 Final classes, methods and variables
        11.7 Interfaces, enumerated types, static import statement

5. JavaScript Language Exam
1.0.0 JavaScript Technology
2.0.0 Analyzing Problems and Designing Solutions
3.0.0 Developing and Testing JavaScript Technology
4.0.0 Declaring, Initializing, and using Variables
5.0.0 Creating and using Objects
6.0.0 Operators and Decision Constructs
7.0.0 Methods
8.0.0 Encapsulation and Constructors
9.0.0 Arrays
10.0.0 Inheritance
11.0.0 Object-oriented Programming
12.0.0 Advanced programming with JavaScript
13.0.0 Frameworks