ICAP CAF CAF-3: Data, Systems and Risks Syllabus

CAF-3: Data, Systems and Risks syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for CAF-3: Data, Systems and Risks in ICAP CAF, not a summary of it.

1. 1

   Data Fundamentals

   2 topics

   • Types, sources and collection of data
   • Data governance, classification and management
2. 2

   Data Analytics and Big Data

   2 topics

   • Stages of data analytics
   • Big Data characteristics and applications
3. 3

   Database Management

   3 topics

   • Normalization and data warehousing
   • ETL (Extract, Transform, Load)
   • Database management systems
4. 4

   IT Systems Architecture

   4 topics

   • Hardware, software and networks
   • ICT for organizational efficiency
   • Adequacy of systems, processes and controls
   • ERP systems and cloud computing
5. 5

   IT Governance and Emerging Technologies

   3 topics

   • IT governance frameworks and ROI
   • Emerging technologies
   • Digital disruption and the accountancy profession
6. 6

   Risks and Controls

   4 topics

   • Risks in physical and digital IT environments
   • Cyber and information security risks
   • IT general controls
   • Frameworks and regulations

CAF-3: Data, Systems and Risks flashcards for ICAP CAF

20 of 50 cards from the CAF-3: Data, Systems and Risks deck — real questions with worked answers.

1. What is the difference between qualitative and quantitative data?

   Quantitative data is numerical and measurable (e.g. sales figures, quantities). Qualitative data is descriptive/non-numerical, describing qualities or characteristics (e.g. opinions, colours, customer feedback).

2. Distinguish between primary and secondary data sources.

   Primary data is collected first-hand by the organisation for a specific purpose (e.g. surveys, interviews, observations). Secondary data already exists, collected by someone else for another purpose (e.g. published reports, government statistics, databases).

3. What is the difference between internal and external data sources?

   Internal data is generated within the organisation (accounting records, HR data, production logs). External data comes from outside (market reports, competitor data, economic statistics, social media).

4. What is the difference between structured, unstructured and semi-structured data?

   Structured data is organised in a defined format such as rows and columns in a database. Unstructured data has no predefined model (emails, images, videos, text). Semi-structured data has some organisational tags or markers but is not in a strict table format (XML, JSON).

5. List common methods of data collection.

   Surveys/questionnaires, interviews, observation, focus groups, experiments, document/record review, web scraping, sensors/IoT, and automated transaction capture.

6. What is data governance?

   The overall framework of policies, processes, roles, standards and controls that ensure data is accurate, available, consistent, secure and used appropriately across the organisation.

7. Define a data owner and a data steward.

   A data owner is accountable for a data set, its quality, classification and access decisions (usually a senior manager). A data steward is responsible for the day-to-day management, quality and proper use of the data.

8. What are the four common levels of data classification?

   Public, Internal (or internal use only), Confidential, and Restricted/Highly Confidential (Secret). Classification determines the level of protection and access controls applied.

9. List the key dimensions of data quality.

   Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness (no duplication), and Reliability/Integrity.

10. What is the data life cycle?

    The stages data passes through: Creation/capture, Storage, Usage/processing, Sharing, Archiving, and Destruction/disposal.

11. What are the typical stages of data analytics?

    1) Define objective/question, 2) Data collection, 3) Data cleaning/preparation, 4) Data analysis, 5) Interpretation, 6) Visualisation/communication, and 7) Action/decision-making.

12. Distinguish descriptive, diagnostic, predictive and prescriptive analytics.

    Descriptive: what happened. Diagnostic: why it happened. Predictive: what is likely to happen. Prescriptive: what should be done about it (recommends actions).

13. What is data cleaning (data cleansing)?

    The process of detecting and correcting (or removing) inaccurate, incomplete, duplicated or improperly formatted data to improve data quality before analysis.

14. What are the 'V' characteristics of Big Data?

    Volume (huge amounts), Velocity (high speed of generation/processing), Variety (different types/formats), Veracity (trustworthiness/quality), and Value (usefulness).

15. Give examples of Big Data applications in business.

    Customer behaviour analysis and targeted marketing, fraud detection, risk management, predictive maintenance, supply chain optimisation, credit scoring, and personalised recommendations.

16. What is data normalization in databases?

    The process of organising data in a database to reduce redundancy and improve data integrity by dividing data into related tables and defining relationships between them.

17. What does First Normal Form (1NF) require?

    That each table cell contains a single (atomic) value and each record is unique, with no repeating groups or arrays in columns.

18. What does Second Normal Form (2NF) require?

    The table must be in 1NF and all non-key attributes must be fully dependent on the entire primary key (no partial dependency on part of a composite key).

19. What does Third Normal Form (3NF) require?

    The table must be in 2NF and have no transitive dependencies; non-key attributes must depend only on the primary key, not on other non-key attributes.

20. What is a data warehouse?

    A central repository of integrated, subject-oriented, historical data drawn from multiple sources, structured and optimised for querying, reporting and analysis rather than transaction processing.

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