An analyst has a dataset with a 'Date' column stored as text in the format 'DD/MM/YYYY'. The analyst needs to convert it to the standard 'YYYY-MM-DD' format for a reporting tool. Which transformation should be applied?

Split the date into three columns.

Replace the slashes with hyphens.

Parse the date string and reformat it to the target format.

Cast the text to a date data type.

A data analyst receives a file where each record is a string like 'Name: Alice; Age: 30; City: New York'. The analyst needs to create a table with columns Name, Age, and City. Which transformation should be applied?

Use a regular expression to extract only the ages.

Split the string by semicolons, then split each segment by the colon.

Load the file as a single-column table.

Replace semicolons with commas and load as CSV.

A healthcare analyst receives a text file where each line is: 'PatientID: 12345, Name: John Doe, DOB: 1980-05-15'. The analyst needs to create a table with columns PatientID, Name, and DOB. Which transformation should be applied first?

Split each line by the comma delimiter, then split each segment by the colon.

Replace all colons with commas, then load the file as a CSV.

Use a regular expression to extract only the date values.

Cast the entire line to a string data type.

A sales analyst has two files: 'customers.csv' with customer_id and customer_name, and 'orders.csv' with order_id, customer_id, and order_total. The analyst needs a dataset with customer names and their order totals. Which transformation should be performed?

Append the two files vertically.

Concatenate the two files side by side.

Join the two files on customer_id.

A data analyst imports a CSV file and finds that the 'Age' column contains values like '25', 'thirty', and 'N/A'. The analyst needs to calculate the average age. What should the analyst do first?

Replace 'N/A' with the median age and convert 'thirty' to 30.

Remove all rows that contain 'N/A' or 'thirty'.

Sort the column alphabetically.

Cast the column to an integer data type.

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Domain 2Objective 3

Apply Data Transformation DA0-001 Practice Questions (Page 1)

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

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4concepts

22%of the exam

Questions 1–5

1application · medium
An analyst has a dataset with a 'Date' column stored as text in the format 'DD/MM/YYYY'. The analyst needs to convert it to the standard 'YYYY-MM-DD' format for a reporting tool. Which transformation should be applied?
Select an answer first
2application · medium
A data analyst receives a file where each record is a string like 'Name: Alice; Age: 30; City: New York'. The analyst needs to create a table with columns Name, Age, and City. Which transformation should be applied?
Select an answer first
3application · medium
A healthcare analyst receives a text file where each line is: 'PatientID: 12345, Name: John Doe, DOB: 1980-05-15'. The analyst needs to create a table with columns PatientID, Name, and DOB. Which transformation should be applied first?
Select an answer first
4application · easy
A sales analyst has two files: 'customers.csv' with customer_id and customer_name, and 'orders.csv' with order_id, customer_id, and order_total. The analyst needs a dataset with customer names and their order totals. Which transformation should be performed?
Select an answer first
5application · medium
A data analyst imports a CSV file and finds that the 'Age' column contains values like '25', 'thirty', and 'N/A'. The analyst needs to calculate the average age. What should the analyst do first?
Select an answer first

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