A data analyst merges two survey datasets. In one dataset, the 'Gender' column has values 'M', 'F', and 'Other'; in the other, it has 'Male', 'Female', and 'Non-binary'. The analyst needs to analyze gender distribution. What should the analyst do?

Convert all values to uppercase.

Remove all rows with 'Other' or 'Non-binary'.

Create a mapping to standardize the values to a common set (e.g., 'Male', 'Female', 'Other').

Use the values as they are and treat 'M' and 'Male' as separate categories.

A bank has two tables: 'customers' with customer_id and customer_name, and 'accounts' with account_id, customer_id, and account_type. An analyst needs a dataset with customer names and their account types. Which transformation should be used?

Merge the tables on account_id.

Concatenate the customer_name and account_type columns.

Join the two tables on customer_id.

Append the two tables vertically.

What is the primary goal of data formatting?

To remove inaccuracies and inconsistencies

To combine datasets from different sources

To standardize data into a consistent format

To extract and interpret data from various formats

A logistics company has two datasets: 'shipments.csv' with one row per shipment (including a shipment_id) and 'delays.csv' with one row per delayed shipment (also including shipment_id). An analyst needs a single dataset containing all shipments, with delay information included where available. Which approach should be used?

Perform an inner join on shipment_id.

Append the two datasets vertically.

Perform a left join on shipment_id, with shipments as the left table.

Perform a full outer join on shipment_id.

CompTIAData+

Domain 2Objective 3

Apply Data Transformation DA0-001 Practice Questions (Page 4)

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 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

4concepts

22%of the exam

Questions 16–20

16expert · medium
A data analyst merges two survey datasets. In one dataset, the 'Gender' column has values 'M', 'F', and 'Other'; in the other, it has 'Male', 'Female', and 'Non-binary'. The analyst needs to analyze gender distribution. What should the analyst do?
Select an answer first
17application · easy
A bank has two tables: 'customers' with customer_id and customer_name, and 'accounts' with account_id, customer_id, and account_type. An analyst needs a dataset with customer names and their account types. Which transformation should be used?
Select an answer first
18foundation · easy
A data analyst discovers that a customer database contains multiple records with the same customer ID but different spellings of the customer's name. Which data transformation process is most directly used to address this issue?
Select an answer first
19foundation · easy
What is the primary goal of data formatting?
Select an answer first
20application · medium
A logistics company has two datasets: 'shipments.csv' with one row per shipment (including a shipment_id) and 'delays.csv' with one row per delayed shipment (also including shipment_id). An analyst needs a single dataset containing all shipments, with delay information included where available. Which approach should be used?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to DA0-001

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by CompTIA. “DA0-001” is a trademark of its owner, used for identification only.

Apply Data Transformation DA0-001 Practice Questions (Page 4)

Questions 16–20

A data analyst merges two survey datasets. In one dataset, the 'Gender' column has values 'M', 'F', and 'Other'; in the other, it has 'Male', 'Female', and 'Non-binary'. The analyst needs to analyze gender distribution. What should the analyst do?

A bank has two tables: 'customers' with customer_id and customer_name, and 'accounts' with account_id, customer_id, and account_type. An analyst needs a dataset with customer names and their account types. Which transformation should be used?

A data analyst discovers that a customer database contains multiple records with the same customer ID but different spellings of the customer's name. Which data transformation process is most directly used to address this issue?

What is the primary goal of data formatting?

Keep studying — Data acquisition and preparation