DA0-002 Data Concepts and Environments Practice Question
A data analyst is troubleshooting a report that shows unusually high sales for a specific product. Upon investigation, the analyst finds that the product was returned by several customers, but the returns were recorded in a separate system and not reflected in the sales data. Which data integration concept was likely missing?
⚠ Common exam trap
Many exam-takers confuse the data movement process (ETL) with the data validation process (reconciliation), assuming that simply extracting and loading data will automatically ensure consistency between separate systems.
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 reconciliation
The core issue is that the sales data and returns data are inconsistent because they were not cross-verified. Data reconciliation is the process of comparing datasets to ensure they are in agreement and identifying discrepancies, such as returns not being reflected in sales figures. Without reconciliation, the analyst would not detect that the high sales number is inflated by unrecorded returns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ETL (Extract, Transform, Load)
Why it's wrong here
ETL extracts, transforms and loads data between systems; it is the mechanism that would have moved returns into the sales dataset. Its absence explains the gap, but the question asks which concept was missing, and ETL describes the pipeline rather than the cross-system ownership and definition of returns data.
- ✓
Data reconciliation
Why this is correct
Sales figures were never adjusted for returns held in a separate system, so the two datasets disagreed. Data reconciliation compares and aligns records across sources to detect and correct such mismatches, which would have surfaced the unreflected returns before the report ran.
- ✗
Data profiling
Why it's wrong here
Data profiling examines datasets for anomalies, nulls and value distributions; it would flag odd sales figures but cannot merge returns from another system into the report. Profiling is correct when assessing source data quality before migration, not for reconciling records across systems.
- ✗
Data governance
Why it's wrong here
Data governance defines ownership, definitions and policies for data assets; it would assign accountability for returns data but performs no physical integration. Governance is correct when establishing stewardship, standards and access rules, not for combining records from separate operational systems.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.