Quick Answer
The answer is data aggregation, as it is the most appropriate technique for handling data granularity differences in data integration. By rolling up hourly sales data to a daily level using functions like SUM or AVG during the ETL process, you ensure all source data is stored at a common grain in the data warehouse, which is essential for consistent reporting and analysis. On the CompTIA Data+ DA0-001 exam, this concept tests your understanding of how to resolve granularity mismatches during the integration phase, often appearing in scenario-based questions where you must choose between aggregation, filtering, or joining. A common trap is selecting data filtering, which removes data rather than standardizing its level of detail. Remember the mnemonic “Aggregate to Align”—when grains differ, always aggregate the finer-grained data up to the coarser level.
This DA0-001 practice question tests your understanding of mining and acquiring data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Data aggregation
Aggregation rolls up data to a consistent level.
Data normalization
Why wrong: Normalization restructures tables to reduce redundancy, not granularity.
Data deduplication
Why wrong: Deduplication removes duplicates, unrelated to granularity.
Data profiling
Why wrong: Profiling identifies issues but does not resolve granularity.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 up data to a consistent level.
Related concept
Read the scenario before looking for a memorised answer.
Data normalization
Why it's wrong here
Normalization restructures tables to reduce redundancy, not granularity.
Data deduplication
Why it's wrong here
Deduplication removes duplicates, unrelated to granularity.
Data profiling
Why it's wrong here
Profiling identifies issues but does not resolve granularity.
Common exam traps
The trap here is that candidates may confuse data normalization (a schema design concept) with the need to standardize data granularity, leading them to incorrectly select normalization instead of aggregation.
Detailed technical explanation
In practice, handling granularity differences often involves using window functions or GROUP BY clauses in SQL to aggregate finer-grained data to a coarser level. For example, hourly timestamps can be truncated to the date part and then summed to produce daily totals. A real-world scenario is integrating point-of-sale transactions (hourly) with inventory snapshots (daily), where aggregation ensures both datasets align on the same time axis for inventory turnover calculations.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Mining and Acquiring Data — This question tests Mining and Acquiring Data — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DA0-001 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 DA0-001 exam.
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