DA0-002 Visualization and Reporting Practice Question
A data analyst is designing a self-service reporting platform for business users. Which TWO practices will help ensure data consistency and trust? (Select TWO.)
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
✓
Implement a single version of truth
Option A (Implement a single version of truth) is correct because consolidating reporting on one governed, authoritative dataset (e.g., a curated gold layer or semantic model) eliminates conflicting figures that arise when users pull from multiple duplicated sources, directly ensuring consistency and trust. Option E (Provide a data dictionary for all metrics) is correct because documenting each metric's exact definition, calculation, source column, and owner gives business users a shared, unambiguous meaning for every KPI, preventing misinterpretation and reinforcing confidence in the numbers. Options B, C, and D do not belong: letting users create their own data sources (B) proliferates ungoverned, inconsistent datasets; granting row-level security to all users (C) is an access-control measure that does not by itself guarantee metric consistency; and removing data lineage tracking (D) destroys the auditability and traceability that underpin trust in the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a single version of truth
Why this is correct
A single version of truth consolidates metrics and definitions into one governed dataset, eliminating conflicting figures across reports. This directly satisfies the consistency and trust requirement, since business users draw from identical, curated data rather than divergent extracts.
- ✗
Allow users to create their own data sources
Why it's wrong here
User-authored data sources bypass governed, certified connections, producing divergent extracts and conflicting figures across reports. It is tempting because it accelerates self-service, but it is correct only in exploratory sandboxes where consistency across shared reporting is not required.
- ✗
Grant row-level security to all users
Why it's wrong here
Row-level security filters which rows each user sees; it does not standardise definitions or values across reports, so business users still see conflicting figures. It is tempting because RLS genuinely enforces per-user data access, and would be correct when the requirement is restricting users to their own region's or department's records.
- ✗
Remove all data lineage tracking
Why it's wrong here
Discarding lineage removes the ability to trace metrics to source systems, so errors cannot be diagnosed and trust erodes. It is tempting when simplifying the platform, but lineage is correctly retained where auditability and impact analysis across transformations are required.
- ✓
Provide a data dictionary for all metrics
Why this is correct
A data dictionary documents each metric's definition, calculation, source and owner, so every business user interprets figures identically. This directly satisfies the consistency and trust requirement by removing ambiguous or conflicting metric definitions across the self-service platform.
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Written by Johnson Ajibi, MSc IT Security
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