Microsoft Power BI Data Analyst358 questions across 36 pages are complete and free, with no account or card.
358 questions in this bank
Free access · progress saved on this device
You have a Microsoft Fabric eventhouse named Eventhouse1 that contains high-velocity and high-volume data from IoT devices. Eventhouse1 can be queried to deliver data to reports in near-real-time (NRT). You plan to use Power BI Desktop to create a report that will contain both historical and live data. You need to connect to Eventhouse1. The solution must minimize development effort. What should you do?
Answer: A
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?
Answer: A
There are two requirements to the question - improve the performance and enable the required analysis. Removing the GUID column will do exactly that - it will improve the performance because it is one less column of data to load but it still enables the required analysis given the IOT ID column is equally unique.
Source: Adapted from community discussion — BillyBobby
You have a Power BI semantic model that contains the following tables.
You need to define a relationship from FactSales to DimProduct. The solution must ensure the fastest DAX query performance. Which cardinality should you use?
Answer: D
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?
Answer: B
Agreeded. Bcause, if you just transform the data type you will not be able to analyse the information by time, as requested.
Source: Adapted from community discussion — GuerreiroJunior
DRAG DROP - You have a Power BI data model that contains two tables named Products and Sales. A one-to-many relationship exists between the tables. You have a report that contains a report-level filter for Products. You need to create a measure that will return the percent of total sales for each product. The measure must respect the report-level filter when calculating the total. How should you complete the DAX measure? To answer, drag the appropriate DAX functions to the correct targets. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
The tricky thing here is that the report contains a report-level filter for Products and you need to calculate all sales (for all products) for the divisor. So I'll share here both functions definition from dax.guide: ALLSELECTED: Returns all the rows in a table, or all the values in a column, ignoring any filters that might have been applied inside the query, but keeping filters that come from outside. ALL: Returns all the rows in a table, or all the values in a column, ignoring any filters that might have been applied. So, for me the correct answer is CALCULATE & ALL
Source: Adapted from community discussion — romer0
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.
Answer: AC
AC is the correct answer. According to https://learn.microsoft.com/en-us/power-bi/transform-model/desktop-date-tables, marking a table as a Date Table automatically removes the auto-generated date table.
Source: Adapted from community discussion — glenman0202
You have a Power BI semantic model that contains a table named Table1. Table1 runs in Import storage mode. You need to update the model to change the storage mode of Table1 to DirectQuery. What should you do?
Answer: A
HOTSPOT - 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.
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
&& is used as a Replacement of 'AND' function. Whereas & is used to concatenate two strings. So answer will be "If - &".
Source: Adapted from community discussion — Male777
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 create a custom column that concatenates the IoT GUID column and the IoT ID column and then delete the IoT GUID and IoT ID columns. Does this meet the goal?
Answer: B
No, creating a custom column that concatenates the IoT GUID and IoT ID columns and then deleting the IoT GUID and IoT ID columns will not help in analyzing IoT events by the hour and day of the year. The solution does not address the need to extract the hour and day of the year from the DateTime column, which is essential for the required analysis. To analyze IoT events by the hour and day of the year, you need to extract the hour and day of the year from the DateTime column and create new columns for them. This can be done using the Power Query Editor in Power BI. Once the new columns are created, you can use them to group and aggregate the data by hour and day of the year, respectively. This approach will also help to improve dataset performance by reducing the amount of data that needs to be processed.
Source: Adapted from community discussion — Abhi256
You have a custom connector that returns ID, From, To, Subject, Body, and Has Attachments for every email sent during the past year. More than 10 million records are returned. You build a report analyzing the internal networks of employees based on whom they send emails to. You need to prevent report recipients from reading the analyzed emails. The solution must minimize the model size. What should you do?
Answer: B
B is the best option to prevent report recipients from reading the analyzed emails and minimize the model size. Removing the Subject and Body columns during the import process ensures that they are not included in the model, which prevents report recipients from being able to access the analyzed email content. A and C are not effective solutions for preventing report recipients from reading the analyzed emails. A: Hiding the Subject and Body columns in the model view may prevent report recipients from seeing the content in the report, but the data is still stored in the model and can potentially be accessed by someone with the appropriate permissions. C: Implementing row-level security (RLS) restricts data access based on user roles or permissions, but it does not prevent access to the analyzed email content in the model. No confusion, and no need to discuss further
Source: Adapted from community discussion — RazaTheLegend
Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page
PL-300 practice, question 141. 358 questions available.
Version 1.0.0. How we source questions