Which data attribute should be checked to prevent data aggregation in big data?
When choosing data sources to be used within a big data architecture, which of the following data attributes MUST be considered to ensure data is not aggregated?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests whether you recognize that granularity, not data quality attributes like accuracy or consistency, determines whether source data is already aggregated; conflating quality with data detail is the classic trap.
In big data architecture, ensuring that data is not aggregated requires selecting data sources with sufficient detail; the correct attribute is granularity. Community consensus strongly favors granularity, as it directly controls the level of data detail and prevents unintended aggregation.
Selecting Accuracy, because it is widely considered essential for reliable data; however, data can be accurate yet still aggregated, so accuracy does not ensure the data is unaggregated.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Granularity is the attribute that defines the level of detail in a data source. To avoid aggregated data, the source must contain data at a sufficiently detailed, unaggregated level. As a community comment notes, “If you want to avoid aggregated data, you must ensure that the data is collected and stored at a more granular (detailed) level.” Without high granularity, any source could already be summarized, making it impossible to analyze underlying records.Why the Other Options Are Wrong
Accuracy, consistency, and reliability are all data quality criteria that ensure the data is trustworthy, but none of them controls whether data has been rolled up. A data source can be perfectly accurate, consistent, and reliable yet be pre-aggregated at a monthly level or by demographic, losing valuable detail. Thus, only granularity directly addresses the requirement of preventing aggregation.Community Comment Notes
The community unanimously votes for B, Granularity. Comment [1] provides a clear explanation about storing data at a granular level to avoid aggregation, while comment [2] simply reaffirms “B. Granularity.” There are no dissenting comments, and no respondent questions the correct answer, showing a strong consensus and straightforward reasoning for this CDPSE-style scenario.Exam Strategy
When you see the phrase “not aggregated,” immediately focus on granularity, which refers to the level of detail in the data. Also, remember that data quality attributes like accuracy, consistency, and reliability are often used as distractors, but they are unrelated to whether data is summarized.
Related Analysis
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