CompTIA Data+ Sample Questions With Full Explanations
Here are 8 multiple-choice questions designed to mirror the difficulty, style, and domain coverage of the CompTIA Data+ (DA0-001) exam. The questions span data governance, analysis, visualization, and SQL, with plausible distractors to challenge your critical thinking.
Question 1
A data analyst is tasked with creating a report that will be used by the marketing department to track campaign performance. During the ETL process, they notice that the source data contains multiple entries for the same customer due to slight variations in name spelling (e.g., "Jon Smith" vs. "John Smith"). Which data quality dimension is primarily being violated? Want to go further? AWS Data Engineer Associate Sample Questions With Full Explanations is a good next step.
A. Validity
B. Uniqueness
C. Timeliness
D. Consistency
Answer: B
Explanation: Uniqueness ensures that no duplicate records exist within a dataset. The scenario describes duplicate customer entries, which directly violates this dimension. Validity refers to data conforming to defined formats or rules, timeliness refers to data being available when needed, and consistency refers to data being the same across different sources.
Question 2
A data analyst needs to combine two datasets: one containing customer IDs and names, and another containing customer IDs and purchase history. They want to keep all records from the customer table and only matching records from the purchase table. Which type of join should they use?
A. INNER JOIN
B. RIGHT JOIN
C. LEFT JOIN
D. CROSS JOIN
Answer: C
Explanation: A LEFT JOIN returns all rows from the left (first) table and the matching rows from the right (second) table, inserting NULLs for non-matching right-side records. An INNER JOIN only returns rows with matches in both tables, a RIGHT JOIN does the opposite of a LEFT JOIN, and a CROSS JOIN produces a Cartesian product of all rows.
Question 3
A data analyst is analyzing a dataset of house prices and finds that the mean is significantly higher than the median. Which of the following is the most likely cause, and which measure of central tendency should be reported to the stakeholders?
A. The data is left-skewed; report the mean.
B. The data is right-skewed; report the median.
C. The data is normally distributed; report the mode.
D. The data contains outliers on the low end; report the mean.
Answer: B Before moving on, consider reading Google Professional Data Engineer Sample Questions With Full Explanations.
Explanation: When the mean is higher than the median, it indicates a right-skewed distribution (long tail of high values) or the presence of high-end outliers. In such cases, the median is more robust and representative of the typical house price because it is not heavily influenced by extreme values. Left-skewed data would pull the mean below the median.
Question 4
A data governance policy requires that all Personally Identifiable Information (PII) stored in a database must be transformed so that it cannot be read by unauthorized users, even if the database is compromised. However, the data must still be usable for analytics (e.g., joining on a unique key). Which of the following techniques best meets this requirement?
A. Data masking
B. Tokenization
C. Data encryption (at rest)
D. Data anonymization
Answer: B
Explanation: Tokenization replaces sensitive data with a unique, non-reversible placeholder (token) that retains the same format and can be used for joins, making it ideal for analytics while protecting the original data. Data masking hides data (e.g., showing "XXX-XX-1234") but is often reversible or alters the data for display only. Encryption protects data but requires decryption keys for use, which can be a security risk during processing. Anonymization removes all identifiers, which would break the ability to join on a unique key.
Question 5
A data analyst is creating a line chart to show the trend of daily website visitors over the past year. However, the chart appears cluttered and difficult to read. Which of the following is the most appropriate action to improve clarity without losing the overall trend?
A. Switch to a pie chart to show the proportion of visitors by month.
B. Aggregate the data to a weekly or monthly level.
C. Remove all data points that are below the average.
D. Use a scatter plot instead of a line chart.
Answer: B
Explanation: Aggregating daily data into weekly or monthly totals smooths out daily noise and reduces the number of data points, making the trend line readable. A pie chart is inappropriate for time-series trends. Removing data points below the average introduces bias and misrepresents the data. A scatter plot would not improve the trend readability and might actually make it worse.
Question 6
An analyst is working with a dataset that has a column for "Temperature" recorded in Celsius. A business rule requires the data to be stored in Fahrenheit. In the ETL process, which step should be used to perform this conversion?
A. Data profiling
B. Data transformation
C. Data validation This topic pairs well with AI-900 Sample Questions With Full Explanations.
D. Data loading
Answer: B
Explanation: Data transformation involves converting data from one format or unit to another (e.g., Celsius to Fahrenheit) to meet business requirements. Data profiling is the process of examining the data to understand its structure and quality. Data validation checks data against rules to ensure accuracy. Data loading is the process of writing data into the target system.
Question 7
A data analyst is performing a hypothesis test to determine if a new website layout increases the average time spent on a page. They set a significance level (alpha) of 0.05. The resulting p-value from the test is 0.03. What is the correct conclusion?
A. Fail to reject the null hypothesis; there is no evidence of a change.
B. Reject the null hypothesis; the new layout has a statistically significant effect.
C. Accept the null hypothesis; the new layout decreases time on page.
D. The p-value is too high; the test is inconclusive.
Answer: B
Explanation: Since the p-value (0.03) is less than the significance level (0.05), the result is statistically significant, and the null hypothesis (no effect) is rejected. This suggests the new layout likely has a real effect on time spent. Failing to reject would occur if p > 0.05. We do not "accept" the null hypothesis; we simply fail to reject it, and the p-value does not indicate the direction of the effect without additional context.
Question 8
Which of the following best describes the purpose of a data dictionary in a data governance framework?
A. It provides a backup and recovery plan for the database.
B. It defines the physical storage structure of the database files.
C. It centralizes metadata, defining the meaning, relationships, and usage of data elements.
D. It lists all the users who have access to the database and their passwords.
Answer: C
Explanation: A data dictionary is a central repository of metadata that defines the logical structure of data, including field names, data types, definitions, and business rules. It ensures consistency and understanding across the organization. It does not handle physical storage, backup plans, or user credentials (which are managed by security policies and database administrators).
These questions cover a broad spectrum of the exam objectives, from data quality and SQL joins to statistical analysis and governance. Remember to focus not just on memorizing terms, but on applying them to real-world scenarios.
If you need structure for that focus, a free CompTIA Data+ study plan gives you a schedule that fits your available time.
Use CompTIA Data+ exam questions to confirm that what you have studied actually translates to correct answers.
For a quick check of weak topics, the free CompTIA Data+ readiness check shows where a little more review could help.