CompTIA DA0-002 Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for CompTIA Data+ V2 (DA0-002) topics such as data types, data quality, cleaning, joins, descriptive statistics, visualization choice, dashboards, governance, ethics, lineage, and reporting. The prompts emphasize analytic integrity before tool choice.

Where these questions fit in the DA0-002 guide

The sample set below is part of the CompTIA Data+ guide path:

• CompTIA certification hub
• DA0-002 exam guide
• DA0-002 cheat sheet
• DA0-002 study plan
• DA0-002 FAQ
• DA0-002 resources
• DA0-002 glossary

DA0-002 data analytics sample questions

Work through each prompt before opening the explanation. Data+ questions usually reward the answer that starts from the business question, validates the data, chooses a suitable method, and communicates limitations.

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Question 1

Topic: Handling missing values

A sales dataset has missing values in the discount field. The missing values are concentrated in one region and one month. What is the strongest first step before cleaning?

• A. Replace every missing discount with zero without documenting the assumption.
• B. Investigate the missingness pattern, source process, affected records, and business meaning before deciding whether to impute, flag, remove, or correct values.
• C. Delete the whole region from the analysis because missing data always makes a row invalid.
• D. Ignore the field and publish the dashboard without a data-quality note.

Best answer: B

Explanation: Missing data should be understood before cleaning. Concentration by region and month suggests a process issue that can bias analysis if handled blindly.

Why the other choices are weaker:

• A imposes an assumption that may be false.
• C may discard useful data unnecessarily.
• D hides a limitation from dashboard users.

What this tests: Data quality, missing values, bias, cleaning decisions, documentation, and source validation.

Related topics: Data quality; Missing values; Cleaning; Bias

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Question 2

Topic: Choosing the right visualization

An executive wants to compare quarterly revenue across five product categories and quickly identify which category grew most. Which visualization is the strongest fit?

• A. A pie chart with all quarters and categories combined into one circle.
• B. A map, even though geography is not part of the question.
• C. A grouped or small-multiple bar chart with clear quarter and category labels.
• D. A table with no sorting, units, or quarter labels.

Best answer: C

Explanation: Comparing categories over quarters needs a visual that supports category comparison and time contrast. A bar-based design is usually clearer than a crowded composition chart.

Why the other choices are weaker:

• A hides quarter-by-quarter comparison.
• B introduces geography when it is not relevant.
• D may contain data but does not communicate the comparison efficiently.

What this tests: Visualization choice, audience, category comparison, trend comparison, and dashboard clarity.

Related topics: Visualization; Bar chart; Executive reporting; Comparison

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Question 3

Topic: Correlation and causation

An analyst finds a strong positive correlation between training attendance and sales performance. A manager wants to state that training caused the higher sales. What should the analyst say?

• A. Correlation always proves causation when the coefficient is high.
• B. The result must be ignored because correlation is never useful.
• C. The analyst should remove all employees who did not attend training.
• D. The data shows an association, but causation requires stronger evidence such as study design, controls, or analysis of confounding factors.

Best answer: D

Explanation: Data+ questions often test interpretation discipline. Correlation can support a hypothesis, but it does not prove cause without additional evidence.

Why the other choices are weaker:

• A makes a classic causation error.
• B dismisses a useful analytical signal.
• C creates selection bias and does not address causation.

What this tests: Correlation, causation, confounding variables, interpretation, and responsible reporting.

Related topics: Statistics; Correlation; Causation; Interpretation

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Question 4

Topic: Sensitive data in a dashboard

A dashboard for managers includes employee-level performance and compensation data. The audience only needs department-level trends. Which governance change is strongest?

• A. Publish employee-level data to all staff because more detail is always better.
• B. Export the raw data to a public file-sharing link for convenience.
• C. Aggregate to department level, restrict access by role, mask or remove unnecessary sensitive fields, and document the data definition and owner.
• D. Remove all labels from the dashboard so viewers cannot identify what data is shown.

Best answer: C

Explanation: Governance should match the business need. Aggregation, minimization, role-based access, and documentation reduce exposure while preserving useful insight.

Why the other choices are weaker:

• A overexposes sensitive data.
• B creates an uncontrolled sharing risk.
• D makes the dashboard misleading rather than protected.

What this tests: Data governance, privacy, aggregation, RBAC, minimization, and dashboard documentation.

Related topics: Governance; Privacy; RBAC; Data minimization

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