DA0-002 Data Acquisition and Preparation Practice Question
A data analyst needs to combine sales data from multiple regional databases with different schemas. Which process is best?
⚠ Common exam trap
The trap is confusing federation/virtualization (query-in-place, no persistence) with ETL (transform-and-persist), causing candidates to pick a lighter-weight option that cannot reconcile schemas.
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
✓
ETL (Extract, Transform, Load)
ETL is correct because it extracts data from each source, transforms it to reconcile differing schemas (column names, types, keys, units), and loads it into a unified target. Schema heterogeneity across regional databases is exactly the transformation problem ETL is designed to solve. The transformed, conformed data can then be queried consistently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data federation
Why it's wrong here
Federation queries sources at runtime, leaving the differing schemas unresolved, so the analyst still cannot present one unified sales view without per-source transformation. It suits real-time access to a handful of heterogeneous sources where no consolidated copy is wanted.
- ✓
ETL (Extract, Transform, Load)
Why this is correct
ETL transforms data before loading, so each regional database's differing schema is reconciled in a staging area first. This directly satisfies the stem's constraint of combining sources with mismatched schemas, producing one consistent target structure. ELT would instead load raw, inconsistent schemas and defer transformation, complicating cross-regional joins.
- ✗
Data replication
Why it's wrong here
Data replication copies data between stores while preserving each source schema, so it does not reconcile differing structures into one queryable result. It is tempting because it moves data across databases, but schema harmonisation and merging require extract, transform, load processing instead.
- ✗
Data virtualization
Why it's wrong here
Virtualisation exposes each regional schema as-is, so the analyst must still reconcile differing structures in every query rather than receiving one combined dataset. It fits querying live sources without copying data when schemas already align.
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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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