DA0-002 Data Concepts and Environments Practice Question
A manufacturing company has two primary data systems: an ERP system that stores production orders with fields like OrderID, ProductID, Quantity, and ProductionDate, and a CRM system that stores customer sales with fields like SaleID, CustomerID, ProductID, SaleDate, and Amount. The data analyst needs to create a unified view of product performance by joining these tables. However, the ProductID field in the ERP uses a 5-character alphanumeric code (e.g., 'P1234'), while the CRM uses a 6-character code (e.g., 'PR1234'). Additionally, some products have multiple entries due to slight variations in naming. The analyst wants to ensure accurate matching without losing data. Which action should the analyst take first to address the data inconsistency?
⚠ Common exam trap
Watch out — candidates often choose fuzzy matching (Option D) thinking it handles all variations, but CompTIA often tests the principle that deterministic mapping is preferred over probabilistic methods when the inconsistency is systematic and can be resolved with a known transformation.
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
✓
Create a mapping table that standardizes ProductID formats between ERP and CRM.
Creating a mapping table allows the analyst to explicitly define the relationship between the 5-character ERP ProductID and the 6-character CRM ProductID, ensuring accurate joins without data loss. This approach standardizes the inconsistent formats and handles variations by providing a controlled, deterministic lookup, which is essential for maintaining referential integrity in a unified view.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a mapping table that standardizes ProductID formats between ERP and CRM.
Why this is correct
Correct: Standardization of keys is necessary before joining.
- ✗
Perform data profiling to identify all unique ProductID values and their frequencies.
Why it's wrong here
Incorrect: Profiling is a diagnostic step, not a corrective action for inconsistency.
- ✗
Aggregate data by product name and ignore ProductID mismatches.
Why it's wrong here
Incorrect: Aggregation loses granularity and may produce incorrect results.
- ✗
Use a fuzzy matching algorithm to join on similar ProductID strings.
Why it's wrong here
Incorrect: Fuzzy matching can help but is less precise without standardization.
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