You have a Power BI dataset and a connected report.
You need to ensure that users can analyze data in Microsoft Excel only by connecting directly to the dataset.
You grant the users the Build permission for the dataset.
What should you do next?
A. Certify the dataset used by the report.
B. Change the default visual interaction for the report.
C. For the report, change the Export data setting to None.
D. For the report, change the Export data setting to Summarized data, data with current layout and underlying data.
Show Answer
Correct Answer: C
Explanation: The requirement states that users must be able to analyze data in Excel only by connecting directly to the Power BI dataset. Granting Build permission allows that direct connection. To prevent any alternative method, you must disable exporting data from the report itself. Setting the report’s Export data option to None ensures users cannot export summarized or underlying data from visuals, forcing Excel analysis to occur exclusively via a live connection to the dataset.
Question 183
You have a Power BI model that contains a table named Employee. The table contains the following data.
Each employee has one manager as shown in the ParentEmployeeID column.
All reporting paths lead to the CEO at the top of the organizational hierarchy.
You need to create a calculated column that returns the count of levels from each employee to the CEO.
Which DAX expression should you use?
A. PATHLENGTH(PATH(Employee[EmployeeID],Employee[ParentEmployeeID]))
B. PATHITEM(PATH(Employee[EmployeeID],Employee[ParentEmployeeID]),1,INTEGER)
C. PATHCONTAINS(PATH(Employee[EmployeeID],Employee[ParentEmployeeID]),1)
D. PATH(Employee[EmployeeID],Employee[ParentEmployeeID])
Show Answer
Correct Answer: A
Explanation: To count the number of hierarchy levels from each employee up to the CEO, you must first generate the parent-child path and then count how many nodes are in that path. PATH(Employee[EmployeeID], Employee[ParentEmployeeID]) returns a delimited string of all ancestors including the employee and the CEO. PATHLENGTH(...) returns the number of items in that path, which corresponds to the number of levels. The other options either return a single item, a boolean, or the path string itself rather than a count.
Question 184
HOTSPOT
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You have a Power BI data model that contains a table named Stores. The table has the following columns:
• Store Name
• Open Date
• Status
• State
• City
You need to create a calculated column named Active Store Name that meets the following requirements:
• When the value of the Status column is “A”, the value in the Store Name column must be returned.
• When the value of the Status column is NOT “A”, the value in the Store Name column that is prefixed with "Inactive - " must be returned.
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: IF
&
Explanation: Use IF to test whether Status equals "A". Use the & operator to concatenate the text prefix with Store Name when the status is not active.
Question 185
You have a Power BI data model that analyzes product sales over time. The data model contains the following tables.
A one-to-many relationship exists between the tables.
The auto date/time option for the data model is enabled.
You need to reduce the size of the data model while maintaining the ability to analyze product sales by month and quarter.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Create a relationship between the Date table and the Sales table.
B. Disable the auto date/time option.
C. Create a Date table and select Mark as Date Table.
D. Disable the load on the Date table.
E. Remove the relationship between the Product table and the Sales table.
Show Answer
Correct Answer: A, C
Explanation: To reduce model size while keeping month and quarter analysis, replace Power BI’s auto-generated date tables with a single, optimized Date table. Creating a dedicated Date table and marking it as a Date Table removes the hidden auto date/time tables. Creating a relationship between the Date table and the Sales table enables time-based analysis using that single Date table, avoiding multiple auto-generated date tables and reducing model size.
Question 186
You have a Microsoft Power BI report. The size of PBIX file is 550 MB. The report is accessed by using an App workspace in shared capacity of powerbi.com.
The report uses an imported dataset that contains one fact table. The fact table contains 12 million rows. The dataset is scheduled to refresh twice a day at 08:00 and 17:00.
The report is a single page that contains 15 AppSource visuals and 10 default visuals.
Users say that the report is slow to load the visuals when they access and interact with the report.
You need to recommend a solution to improve the performance of the report.
What should you recommend?
A. Change any DAX measures to use iterator functions.
B. Remove unused columns from tables in the data model.
C. Replace the default visuals with AppSource visuals.
D. Increase the number of times that the dataset is refreshed.
Show Answer
Correct Answer: B
Explanation: The primary issue is report rendering and interaction performance in a shared capacity with a very large imported model (550 MB, 12 million rows). Reducing the data model size is a best‑practice and directly improves performance by lowering memory usage, storage engine scans, and query execution time. Removing unused columns reduces cardinality and compression overhead, which speeds up visual load and interaction. The other options do not address the root cause: iterator functions often worsen performance, AppSource visuals are generally slower than default visuals, and increasing refresh frequency has no impact on report interactivity.
Question 187
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
From Power Query Editor, you profile the data shown in the following exhibit.
The IoT GUID and IoT ID columns are unique to each row in the query.
You need to analyze IoT events by the hour and day of the year. The solution must improve dataset performance.
Solution: You change the IoT DateTime column to the Date data type.
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: Changing the IoT DateTime column to the Date data type removes the time component. This prevents analysis by hour, which is explicitly required, and therefore does not meet the goal even if it might slightly reduce cardinality.
Question 188
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
From Power Query Editor, you profile the data shown in the following exhibit.
The IoT GUID and IoT ID columns are unique to each row in the query.
You need to analyze IoT events by the hour and day of the year. The solution must improve dataset performance.
Solution: You remove the IoT GUID column and retain the IoT ID column.
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: The goal has two parts: enable analysis by hour and day of year, and improve performance. Removing the IoT GUID column reduces model size and improves performance. Because the IoT ID column is also unique per row, removing the GUID does not affect the ability to analyze events by time (which depends on the DateTime column, not the identifier). Therefore, the solution meets the goal.
Question 189
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
From Power Query Editor, you profile the data shown in the following exhibit.
The IoT GUID and IoT ID columns are unique to each row in the query.
You need to analyze IoT events by the hour and day of the year. The solution must improve dataset performance.
Solution: You split the IoT DateTime column into a column named Date and a column named Time.
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: Yes. Splitting a combined DateTime column into separate Date and Time columns in Power Query (before loading into the model) reduces column cardinality and improves VertiPaq compression. This supports analysis by day and hour while improving dataset performance, which meets the stated goal.
Question 190
You have the Power BI data model shown in the following exhibit.
The Sales table contains records of sales by day from the last five years up until today’s date.
You plan to create a measure to return the total sales of March 2021 when March 2022 is selected.
Which DAX expression should you use?
A. Calculate (Sum(Sales[Sales]), PREVIOUSYEAR( dimDate[Date])
B. TOTALYTD (SUM(Sales[Sales]), dimDate[Date] )
C. Calculate (SUM(Sales[Sales]), SAMEPERIODLASTYEAR(dimDate[Date] ))
D. SUM(Sales[Sales])
Show Answer
Correct Answer: C
Explanation: When March 2022 is selected, the requirement is to return sales for the same period one year earlier (March 2021). SAMEPERIODLASTYEAR shifts the current date context back exactly one year, preserving the same range of dates (March only). PREVIOUSYEAR would return all dates from the entire prior year, not just March, TOTALYTD calculates year-to-date values, and a simple SUM does not apply any time intelligence.
Question 191
HOTSPOT
-
You are creating a quick measure as shown in the following exhibit.
You need to create a monthly rolling average measure for Sales over time.
How should you configure the quick measure calculation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Base value: Total Sales
Date: Date
Period: Months
Explanation: A monthly rolling average requires averaging the Sales measure over time using the date column, with the period set to Months so the calculation rolls month by month.
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