The correct first action is to investigate the source system to understand why emails are missing. This is because the core principle of data quality remediation is root cause analysis for null values—you must identify the systemic failure before applying any fix. Whether the nulls stem from a failed ETL join, a missing required field in the source application, or a bug in data entry, skipping this step means you are treating symptoms, not causes. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of the data governance hierarchy: always fix the source before the symptom. A common trap is jumping to deletion or placeholder insertion, which can mask a recurring issue and introduce bias into downstream analytics. Remember the memory tip: “Source first, symptom last”—if you see nulls, trace them back to their origin before you act.
DA0-001 Communicating Data Insights Practice Question
This DA0-001 practice question tests your understanding of communicating data insights. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Data Quality Check Report
Table: customer_info
Check: Non-null validation on email field
Result: Failed - 1200 records have null email
Refer to the exhibit. A data analyst is reviewing a data quality report. Which of the following actions should the analyst take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Investigate the source system to understand why emails are missing.
Option C is correct because the first step in data quality remediation is root cause analysis. Without understanding why 1200 records have null emails (e.g., a source system bug, a failed ETL join, or a missing required field), any corrective action like deletion or placeholder insertion risks introducing bias or masking a systemic issue. Investigating the source system aligns with the data governance principle of 'fix the source, not the symptom.'
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Delete the 1200 records with null emails.
Why it's wrong here
Incorrect. Deleting records without understanding impact is risky.
✗
Fill null emails with a placeholder.
Why it's wrong here
Incorrect. Placeholder values can mislead analysis and should only be used after understanding.
✓
Investigate the source system to understand why emails are missing.
Why this is correct
Correct. Root cause analysis should precede any corrective action.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ignore the nulls as they are not critical.
Why it's wrong here
Incorrect. Ignoring data quality issues can lead to downstream problems.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the principle that 'fix the source, not the symptom'—the trap here is that candidates jump to data cleansing actions (delete, fill, ignore) without first diagnosing why the nulls exist, which is a classic data quality management mistake.
Detailed technical explanation
How to think about this question
In data quality frameworks like the DAMA-DMBOK, the 'corrective action' phase always begins with root cause analysis—often querying source system logs, ETL job audit trails, or database constraints (e.g., NOT NULL violations). For example, if the email field is nullable in the source schema but a recent application update stopped populating it, deleting or filling nulls would hide the regression. A real-world scenario: a CRM system missing emails due to a misconfigured API endpoint—only source investigation reveals the fix.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Communicating Data Insights — This question tests Communicating Data Insights — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Investigate the source system to understand why emails are missing. — Option C is correct because the first step in data quality remediation is root cause analysis. Without understanding why 1200 records have null emails (e.g., a source system bug, a failed ETL join, or a missing required field), any corrective action like deletion or placeholder insertion risks introducing bias or masking a systemic issue. Investigating the source system aligns with the data governance principle of 'fix the source, not the symptom.'
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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