DA0-002 Data Governance Practice Question
A data analyst discovers an anomaly in a dataset. Which two actions should be taken before reporting? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to confuse 'immediate reporting' with proactive communication, but CompTIA Data+ expects the understanding that data validation must precede any stakeholder notification to maintain data credibility.
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
✓
Verify the data source and extraction process
Option C is correct because verifying the data source and extraction process ensures the anomaly is not an artifact of how the data was pulled, transformed, or joined before it is escalated. Option D is correct because checking for data entry errors or technical glitches (e.g., sensor faults, ETL failures, or duplicate records) confirms whether the anomaly reflects a genuine signal or a data quality issue. Both steps establish validity and reproducibility of the finding, which is essential prior to reporting. Option A is wrong because assuming the anomaly is real without validation risks reporting a false positive. Option B is wrong because alerting all stakeholders immediately is premature before the anomaly is verified. Option E is wrong because removing the anomaly without documentation destroys evidence and violates data integrity and auditability practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Assume the anomaly is real and report it
Why it's wrong here
Treating an anomaly as genuine without verification risks publishing fabricated findings built on corrupt or duplicated records. It is tempting because anomalies sometimes reveal real events, and reporting it would be correct once validated through source checks or stakeholder confirmation, but assumption bypasses the investigation the stem requires before reporting.
- ✗
Immediately alert all stakeholders
Why it's wrong here
Alerting all stakeholders before validating the anomaly risks spreading false alarms and erodes trust if the data proves correct. Notification is tempting because incident communication is a genuine duty, but it belongs after verification and impact assessment, not as a first investigative step.
- ✓
Verify the data source and extraction process
Why this is correct
Verifying the source and extraction process confirms the anomaly reflects real data rather than a pipeline fault, satisfying the need to validate findings before reporting. Checking lineage, transformation logic and load errors distinguishes genuine outliers from artefacts introduced during ETL, preventing false alarms reaching stakeholders.
- ✓
Check for data entry errors or technical glitches
Why this is correct
Checking for data entry errors or technical glitches directly addresses the anomaly's most likely origin before escalation, satisfying the stem's requirement to validate findings prior to reporting. Verifying whether the deviation stems from transcription mistakes, parsing faults, or pipeline failures distinguishes genuine insight from noise, preventing analysts from reporting spurious results.
- ✗
Remove the anomaly without documentation
Why it's wrong here
Deleting an anomaly silently destroys the audit trail and prevents reviewers from validating whether it reflects a data-entry error, fraud or a genuine event. It is tempting because it produces a clean dataset quickly, and it would be acceptable only after documented investigation and approval, but undocumented removal breaches analytical integrity and reproducibility.
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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.