A human resources model imports employee data with columns for employee ID, name, department, salary, and a photo column with binary data. The report only shows department and salary averages. The model is large and refresh is slow. What should you do?

Remove the employee name column instead of the photo column.

Keep the photo column but hide it from report view.

Remove the photo column from the import.

Convert the photo column to text to reduce its size.

A marketing team imports a web analytics table that records every page view with a timestamp to the second. The report needs to show daily page views by page URL. The table also contains a 'UserAgent' column that is not used. The model is large and slow to refresh. What should you do to reduce the model size while still supporting the report?

Import the raw page view data and use a calculated table to create daily counts.

Keep the raw page view data but remove the 'UserAgent' column and create a measure for daily counts.

Use a row-level security (RLS) rule to limit the page view data to the current day for all users.

Aggregate the page views to daily counts per page URL before importing, and remove the 'UserAgent' column.

What is the primary purpose of analyzing DAX query performance?

To change the data type of columns in the model.

To remove unnecessary rows from the data model.

To identify bottlenecks in measures and relationships that slow down report rendering.

To increase the number of visuals on a report page.

Which of the following is the primary benefit of removing unnecessary rows from a Power BI data model?

Reduced model size and faster query performance.

Increased number of relationships in the model.

Simplified DAX formulas by making them shorter.

Improved data accuracy by adding more detail.

A marketing analytics model imports web analytics data with a row for every page view. The report shows daily unique visitors and page views by campaign. The data includes bot traffic that is not relevant. What should you do to improve model performance?

Filter out bot traffic in Power Query before loading the data.

Keep bot traffic but add a column to flag it.

Aggregate the data to daily level before filtering out bots.

Remove the campaign column to reduce the number of columns.

Microsoft Certified:Power BI Data Analyst Associate

Domain 2Objective 3

Optimize Model Performance PL-300 Practice Questions (Page 3)

Part of the Model 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

5concepts

25–30%of the exam

Questions 11–15

11application · medium
A human resources model imports employee data with columns for employee ID, name, department, salary, and a photo column with binary data. The report only shows department and salary averages. The model is large and refresh is slow. What should you do?
Select an answer first
12application · medium
A marketing team imports a web analytics table that records every page view with a timestamp to the second. The report needs to show daily page views by page URL. The table also contains a 'UserAgent' column that is not used. The model is large and slow to refresh. What should you do to reduce the model size while still supporting the report?
Select an answer first
13foundation · easy
What is the primary purpose of analyzing DAX query performance?
Select an answer first
14foundation · easy
Which of the following is the primary benefit of removing unnecessary rows from a Power BI data model?
Select an answer first
15application · medium
A marketing analytics model imports web analytics data with a row for every page view. The report shows daily unique visitors and page views by campaign. The data includes bot traffic that is not relevant. What should you do to improve model performance?
Select an answer first

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