DA0-002 Data Concepts and Environments Practice Question
A healthcare provider needs to integrate patient data from multiple clinics into a single data warehouse. Which process is used to extract, transform, and load the data?
⚠ Common exam trap
Candidates often confuse ETL with ELT, where candidates assume ELT is always better due to modern big data tools, but the question explicitly describes a traditional data warehouse integration requiring pre-load transformations.
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
ETL (Extract, Transform, Load) is the correct process because the healthcare provider must first extract data from multiple source clinics, then transform it (e.g., standardize formats, clean duplicates, apply business rules) before loading it into the target data warehouse. This ensures data quality and consistency, which is critical for clinical analytics and 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.
- ✗
ELT
Why it's wrong here
ELT loads raw data into the target before transforming it there, whereas this scenario specifies transformation occurring before loading into the warehouse. It is tempting because ELT suits cloud warehouses with strong compute; it would be correct where the target platform performs the transformation, not where transformation precedes loading.
- ✓
ETL
Why this is correct
ETL extracts data from the separate clinic sources, transforms it into a consistent format, and loads it into the central warehouse. This three-stage pipeline directly satisfies the integration requirement, unlike ELT, which loads raw data before transforming.
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OLAP
Why it's wrong here
OLAP supports analytical querying and multidimensional aggregation over loaded data, not the extraction, transformation and loading process itself. It is tempting because the warehouse serves analytical workloads; OLAP would be the correct answer when describing how analysts query aggregated data, not how it is integrated.
- ✗
OLTP
Why it's wrong here
OLTP handles high-volume transactional reads and writes for day-to-day operations, not the batch extraction, transformation and loading of source systems into a warehouse. It is tempting because clinics run OLTP systems as data sources; OLTP would be the correct answer when describing the operational database generating the records, not the integration process.
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