CDS Cheat Sheet 2026

The 30 highest-yield CDS facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. A data steward discovers that a dataset scheduled for deletion under the retention policy is subject to a legal hold. What is the correct action? → Suspend the retention schedule and preserve the data until the hold is lifted
2. What does a data steward typically do? → Oversee data standards and quality
3. When should an issue be escalated to a higher support level? → When the issue exceeds your technical expertise or authorization level
4. What is the first step in a systematic troubleshooting approach? → Identify and clearly define the problem symptoms
5. When should architectural decisions be reviewed and potentially revised? → When requirements change significantly or performance targets are not met
6. What makes a report effective for stakeholder communication? → Clear visualization of relevant metrics aligned with business objectives
7. What is the primary benefit of automating repetitive tasks? → Reduced human error and increased consistency of results
8. Why is regular security training important for all staff? → Employees are often the weakest link in security and training reduces human error
9. How does poor data quality affect business intelligence? → It results in inaccurate analytics and poor decisions
10. Which approach to data classification uses automated scanning tools that inspect content and assign categories based on predefined patterns or keywords? → Content-based classification
11. How should configuration changes be tracked? → Through version control systems with documented change rationale
12. Why is trend analysis valuable in monitoring? → It reveals patterns that predict future issues before they become critical
13. When rolling out a new data classification policy, which training approach best ensures consistent adoption across distributed teams? → Role-based training modules with practical examples, job aids, and competency checks
14. What is a key challenge organizations face when implementing enterprise-wide data classification? → Achieving consistent classification across diverse systems, formats, and business units
15. Which regulation is known for setting strict data protection rules in the European Union? → GDPR
16. When integrating systems, what is an API contract? → A formal agreement on data formats, endpoints, and expected behaviors
17. Which of the following is a key component of a data governance framework? → Roles and responsibilities
18. What is metadata in the context of data management? → Information about data such as its source, format, and usage
19. When is automation NOT appropriate? → For one-time tasks or processes requiring complex human judgment
20. What is data cleansing? → Correcting or deleting inaccurate records
21. Which challenge is common in master data management? → Data duplication and inconsistency
22. Which of the following is a key dimension of data quality? → Accuracy
23. What is the 80/20 rule as applied to performance optimization? → 80% of performance gains come from optimizing 20% of the code or configuration
24. What is the first step in performance optimization? → Establish baseline measurements and identify bottlenecks
25. Why is data validation important during integration? → It ensures data accuracy and consistency across connected systems
26. What is the value of isolating variables during troubleshooting? → It identifies the specific cause by changing one factor at a time
27. Why should automation scripts include version control? → To track changes, enable rollbacks, and support collaborative development
28. How should security be incorporated into system architecture? → Integrated from the initial design phase as a foundational requirement
29. What does 'data literacy' mean in the context of stakeholder enablement for data governance? → The ability to read, understand, question, and work effectively with data in one's role
30. What is 'data obsolescence' in the context of lifecycle management? → Data that is no longer accurate, relevant, or useful for its original purpose