DA0-002 Data Acquisition and Preparation Practice Question
An e-commerce company is acquiring product data from multiple supplier APIs. The APIs return JSON with inconsistent field naming conventions. Which data acquisition technique should be applied?
⚠ Common exam trap
Many candidates confuse data transformation with data aggregation or deduplication, assuming any processing step can fix schema inconsistencies, but only mapping and transformation directly address field naming and structure mismatches.
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
✓
Data mapping and transformation
Data mapping and transformation is the correct technique because the JSON responses from different supplier APIs use inconsistent field naming conventions (e.g., 'product_id' vs. 'ProductID'). This technique defines a schema to map source fields to a standardized target format, ensuring data consistency before loading into the company's system. Without transformation, downstream processes like analytics or inventory management would fail due to mismatched field names.
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 compression
Why it's wrong here
Compression reduces payload size for storage or transfer and cannot rename fields. It is the correct choice when bandwidth or storage cost is the constraint, but here the obstacle is mismatched JSON keys requiring transformation rather than shrinking.
- ✓
Data mapping and transformation
Why this is correct
Mapping reconciles differing field names across supplier schemas into one canonical structure, while transformation standardises values. This satisfies the stem's inconsistent naming constraint, since raw ingestion would yield mismatched columns that cannot be joined or queried consistently.
- ✗
Data deduplication
Why it's wrong here
Deduplication removes duplicate records, leaving field names untouched, so the JSON keys remain inconsistent. It is the right technique when the same product appears repeatedly across feeds, not when the problem is differing key names that must be mapped to one schema.
- ✗
Data aggregation
Why it's wrong here
Aggregation combines records across sources into a summary or unified set; it does not reconcile differing field names. Aggregation is correct when computing totals or merging datasets for reporting, whereas schema harmonisation is needed to map supplier fields to a common naming convention.
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