A data analyst is profiling a customer table and finds that the 'Email' column has multiple inconsistencies: some emails have leading/trailing spaces, some have inconsistent domain casing (e.g., 'GMAIL.COM' vs 'gmail.com'), and some are missing the '@' symbol. The analyst needs to clean the column for a mailing campaign. What is the best approach?

Replace spaces with nothing and leave the rest as is.

Use 'Replace Values' to replace 'GMAIL.COM' with 'gmail.com'.

Trim the column, convert to lowercase, and then filter out rows that do not contain '@'.

Change the data type to Text and then sort the column.

Which Power Query Editor feature displays the data type and format of each column in the data preview?

The Data Preview grid's row numbers

A data analyst is cleaning a dataset of employee records. The 'Email' column contains some values that are not valid email addresses (e.g., 'john.doe' or 'jane@'). The analyst needs to ensure that only valid email addresses are included in the report. What should the analyst do?

Remove the Email column entirely.

Use the Conditional Column feature to flag invalid emails and then filter them out.

Use the Replace Values feature to correct the invalid emails.

Use the Text.Contains function in a custom column to validate email format and then filter out invalid rows.

Microsoft Certified:Power BI Data Analyst Associate

Domain 1Objective 2

Profile and Clean the Data PL-300 Practice Questions (Page 2)

Part of the Prepare the data domain, which accounts for 25–30% of the PL-300 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~10–20 in this domain), expect 3–7 from this objective — we provide 35 practice questions to prepare you well beyond it. (estimate)

35questions here

7free pages

7concepts

25–30%of the exam

Questions 6–10

6expert · hard
A data analyst is profiling a customer table and finds that the 'Email' column has multiple inconsistencies: some emails have leading/trailing spaces, some have inconsistent domain casing (e.g., 'GMAIL.COM' vs 'gmail.com'), and some are missing the '@' symbol. The analyst needs to clean the column for a mailing campaign. What is the best approach?
Select an answer first
7foundation · easy
Which Power Query feature identifies errors in a column and allows you to replace them with a specific value?
Select an answer first
8foundation · easy
Which Power Query transformation is commonly used to standardize inconsistent text values like 'yes', 'Yes', and 'YES'?
Select an answer first
9foundation · easy
Which Power Query Editor feature displays the data type and format of each column in the data preview?
Select an answer first
10expert · hard
A data analyst is cleaning a dataset of employee records. The 'Email' column contains some values that are not valid email addresses (e.g., 'john.doe' or 'jane@'). The analyst needs to ensure that only valid email addresses are included in the report. What should the analyst do?
Select an answer first

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Profile and Clean the Data PL-300 Practice Questions (Page 2)

Questions 6–10

Which Power Query feature identifies errors in a column and allows you to replace them with a specific value?

Which Power Query transformation is commonly used to standardize inconsistent text values like 'yes', 'Yes', and 'YES'?

Which Power Query Editor feature displays the data type and format of each column in the data preview?

A data analyst is cleaning a dataset of employee records. The 'Email' column contains some values that are not valid email addresses (e.g., 'john.doe' or 'jane@'). The analyst needs to ensure that only valid email addresses are included in the report. What should the analyst do?

Keep studying — Prepare the data