DA0-002 Data Governance Practice Question
A financial services firm is establishing a data governance program for its customer analytics platform. The chief data officer wants to ensure that data quality issues are detected and resolved systematically. Which two of the following practices are most appropriate for maintaining data quality on an ongoing basis? (Choose two.)
⚠ Common exam trap
The trap here is selecting manual sampling or ad hoc reporting as quality practices, when they are reactive and not systematic enough for ongoing governance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Establishing a data quality issue log with defined severity levels, owners, and service-level agreements for resolution.
Automated data quality rules during ETL and a formal issue log with severity levels and SLAs together provide a systematic, repeatable approach to detecting and resolving data quality problems. The automated rules catch issues early, while the issue log ensures accountability and timely resolution, which are essential for an ongoing governance program in financial services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Establishing a data quality issue log with defined severity levels, owners, and service-level agreements for resolution.
Why this is correct
A formal issue log with severity levels, assigned owners, and SLAs ensures that detected quality problems are tracked, prioritized, and resolved within agreed timeframes. This creates accountability and a repeatable process, which is a core component of an ongoing data governance program for customer analytics.
- ✗
Encrypting all customer data at rest and in transit to prevent unauthorized access.
Why it's wrong here
Encryption protects data confidentiality but does not address data quality dimensions such as accuracy, completeness, or consistency. It is a security control, not a quality control, so it does not help detect or resolve data quality issues in the customer analytics platform.
- ✓
Implementing automated data quality rules that validate completeness, uniqueness, and referential integrity during ETL loads.
Why this is correct
Automated data quality rules applied during ETL catch issues such as missing values, duplicate records, and broken relationships before data enters the analytics platform. This proactive approach prevents bad data from propagating and supports systematic, repeatable quality checks, which is essential for an ongoing governance program in a financial services context.
- ✗
Assigning a data steward to manually review a random sample of records each quarter and document findings in a spreadsheet.
Why it's wrong here
Manual sampling and spreadsheet documentation are reactive and not scalable for ongoing quality management. While it may uncover some issues, it does not provide systematic detection or resolution across the full dataset, and it lacks the automation and coverage needed for a robust governance program in a financial services firm.
- ✗
Relying on business users to report data errors through ad hoc emails to the IT help desk.
Why it's wrong here
Ad hoc email reporting is unstructured and lacks tracking, prioritization, and ownership. It does not provide a systematic method for detecting and resolving data quality issues, and it places the burden on users rather than establishing proactive controls, making it unsuitable for an ongoing governance program.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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