A data analyst is exploring a dataset of website user registrations. The analyst wants to check for both missing values and duplicate user IDs. Which exploration technique would efficiently identify both issues?

Sort the data by user ID and look for blank cells and repeated IDs.

Calculate the mean of the user ID column.

Generate a summary report that counts nulls per column and counts duplicate user IDs.

Create a scatter plot of user ID versus registration date.

A dataset has a large proportion of missing values in a non-critical column. The analyst decides to remove the column entirely. What is the primary reason this approach might be acceptable?

The column is not important for the analysis

The column has no missing values

The column contains only outliers

A financial analyst is examining a dataset of daily stock returns. The dataset contains a few extreme values that are 10 standard deviations from the mean. The analyst needs to decide how to handle these values for a risk model. The values are confirmed to be accurate—they correspond to days of major market events. What is the most appropriate action?

Keep the extreme values because they represent real events that the model should capture.

Remove the extreme values to make the model more stable.

Impute the extreme values with the mean return for those days.

Cap the extreme values at the 99th percentile to reduce their influence.

A data analyst is working with a dataset of customer transactions. The analyst suspects there are duplicate transaction records because the same transaction appears with the same 'transaction_id' and 'amount'. What is the best way to confirm and quantify the duplicates?

Remove all rows where 'transaction_id' appears more than once.

Calculate the total sum of all transaction amounts.

Group by 'transaction_id' and 'amount' and count the number of records in each group.

Sort the data by 'transaction_id' and visually inspect for repeats.

CompTIAData+

Domain 2Objective 2

Perform Data Exploration DA0-001 Practice Questions (Page 5)

Part of the Data acquisition and preparation domain, which accounts for 22% of the DA0-001 exam. CompTIA does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–13 in this domain), expect 3–4 from this objective — we provide 31 practice questions to prepare you well beyond it. (estimate)

31questions here

7free pages

8concepts

22%of the exam

Questions 21–25

21application · medium
A data analyst is exploring a dataset of website user registrations. The analyst wants to check for both missing values and duplicate user IDs. Which exploration technique would efficiently identify both issues?
Select an answer first
22foundation · easy
A dataset has a large proportion of missing values in a non-critical column. The analyst decides to remove the column entirely. What is the primary reason this approach might be acceptable?
Select an answer first
23expert · hard
A financial analyst is examining a dataset of daily stock returns. The dataset contains a few extreme values that are 10 standard deviations from the mean. The analyst needs to decide how to handle these values for a risk model. The values are confirmed to be accurate—they correspond to days of major market events. What is the most appropriate action?
Select an answer first
24application · medium
A data analyst is working with a dataset of customer transactions. The analyst suspects there are duplicate transaction records because the same transaction appears with the same 'transaction_id' and 'amount'. What is the best way to confirm and quantify the duplicates?
Select an answer first
25foundation · easy
A dataset contains both 'First Name' and 'Full Name' columns. What type of data quality issue does this represent?
Select an answer first

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Perform Data Exploration DA0-001 Practice Questions (Page 5)

Questions 21–25

A data analyst is exploring a dataset of website user registrations. The analyst wants to check for both missing values and duplicate user IDs. Which exploration technique would efficiently identify both issues?

A dataset has a large proportion of missing values in a non-critical column. The analyst decides to remove the column entirely. What is the primary reason this approach might be acceptable?

A data analyst is working with a dataset of customer transactions. The analyst suspects there are duplicate transaction records because the same transaction appears with the same 'transaction_id' and 'amount'. What is the best way to confirm and quantify the duplicates?

A dataset contains both 'First Name' and 'Full Name' columns. What type of data quality issue does this represent?

Keep studying — Data acquisition and preparation