Here, the problem is about granularity and detail level of the data:
The organization only captures sales by region, not by store.
This means certain attributes (e.g., store identifier) are not present or not captured at the required level of detail.
In CompTIA Data+ language, this is a case of data attribute limitations:
The data does not include all the necessary attributes (or the required level of those attributes) to perform finer-grained analysis (e.g., store-level performance, store comparisons).
Why the other options are incorrect:
Data accuracy (B): Deals with whether data values are correct/true; here, the issue is not that regional sales are wrong, but that they lack detail.
Data integrity (C): Refers to internal correctness and reliability (e.g., foreign keys, referential integrity, completeness of relationships).
Data consistency (D): Concerns whether data is uniform across systems or over time (e.g., same format, same definitions).
The main issue is not enough attributes / insufficient granularity, which matches Data attribute limitations (A).
CompTIA Data+ Reference (concept alignment):
DA0-001 Exam Objectives – Data quality: completeness, granularity, and attribute sufficiency.
CompTIA Data+ Study Guide – discussions on data attribute limitations and granularity impacting the types of analysis that can be performed.
