DA0-002 Data Acquisition and Preparation Practice Question
An organization is integrating data from multiple sources into a data warehouse. They need to handle differences in data granularity (e.g., daily vs. hourly sales data). Which technique is most appropriate?
⚠ Common exam trap
It's easy for candidates to confuse data normalization (a schema design concept) with the need to standardize data granularity, leading them to incorrectly select normalization instead of aggregation.
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 aggregation
Data aggregation is the correct technique because it allows the organization to roll up hourly sales data to a daily granularity, ensuring consistency when integrating sources with different levels of detail. By applying aggregation functions (e.g., SUM, AVG) during the ETL process, the data warehouse can store all data at a common grain, which is essential for accurate reporting and analysis.
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 aggregation
Why this is correct
Aggregation rolls hourly sales records up to daily totals, aligning finer-grained source data with the coarser warehouse grain. This resolves the daily-versus-hourly mismatch by summarising detail, which is precisely the granularity conflict the scenario describes.
- ✗
Data normalization
Why it's wrong here
Normalization restructures tables to reduce redundancy, not granularity.
- ✗
Data deduplication
Why it's wrong here
Deduplication removes duplicate records; it does not reconcile differing granularity such as daily versus hourly sales, so aggregation or transformation is required instead. It is tempting because it improves warehouse storage and quality, and would be correct when the same rows arrive repeatedly from multiple sources.
- ✗
Data profiling
Why it's wrong here
Profiling only summarises source data — value distributions, null rates, key uniqueness — it does not reconcile differing grains. The stem requires hourly and daily sales to be merged, which needs aggregation or transformation. Profiling would be right when first assessing unfamiliar source data quality before designing the integration.
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This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.