DA0-002 Data Concepts and Environments Practice Question
To consolidate data from multiple operational databases into a central repository for reporting, a company decides to transform data before loading it into the target system. Which data integration approach is being used?
⚠ Common exam trap
Test-takers frequently confuse ETL with ELT, assuming that any transformation before loading is ELT, but the key distinction is that ELT loads raw data first and transforms it later inside the target system, whereas ETL transforms data before it reaches the target.
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)
The scenario describes transforming data before loading it into the target system, which is the defining characteristic of ETL (Extract, Transform, Load). In ETL, data is extracted from source systems, transformed in a staging area (e.g., cleaning, aggregating, joining), and then loaded into the central repository. This approach is commonly used when the target system (e.g., a data warehouse) requires pre-processed, high-quality data for reporting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
ETL (Extract, Transform, Load)
Why this is correct
ETL transforms data in a staging area before writing to the target, so the central repository receives cleansed, conformed records. This matches the stem's constraint that data is transformed before loading, unlike ELT, which loads raw data first.
- ✗
Data virtualization
Why it's wrong here
Data virtualisation creates a logical abstraction layer that queries source systems in real time without physically moving or persisting data, so it cannot perform the transformation step described in the stem, where data is altered before loading into a central repository. This option tempts because it is commonly used for federated reporting across disparate databases, but the scenario explicitly requires a transformation phase that only an ETL (Extract, Transform, Load) approach can execute.
- ✗
Change data capture
Why it's wrong here
Change data capture replicates only inserted, updated and deleted rows from source databases; it is an extraction technique and performs no transformation before loading. It tempts because CDC genuinely supports consolidating multiple operational sources efficiently, but the stem specifies transformation preceding the load.
- ✗
ELT (Extract, Load, Transform)
Why it's wrong here
ELT loads raw data into the target before any transformation occurs, contradicting the stem's requirement to transform before loading. It tempts because ELT suits cloud warehouses with abundant compute, where transformation happens after load using the warehouse's own engine rather than in a staging pipeline.
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