You need to provide a user with the ability to add members to a workspace. The solution must use the principle of least privilege.
Which role should you assign to the user?
A. Viewer
B. Admin
C. Contributor
D. Member
✔ Correct Answer: D. Member 👥🛠️✨
Why:
A Member can add users to a workspace but cannot change workspace settings or delete the workspace — making it the lowest-privilege role that still allows adding members. 🔐👍
❌ Why not the others?
A. Viewer → Read-only, cannot add anyone 👀🚫
B. Admin → Too much power (full control) ❌💥
C. Contributor → Can edit content but cannot add members 🚫
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.
✔ Correct Answer: B. Remove unused columns from tables in the data model. 🗑️📦⚡
Why:
Your PBIX is 550 MB and the model contains 12 million rows.
Removing unused columns shrinks the model size, improves compression, speeds up loading, and reduces visual rendering time. It’s the #1 performance booster in Import models. 🚀
❌ Why not the others?
A. Use iterator functions → Iterators (SUMX, etc.) are slower, not faster ❌🐢
C. Replace default visuals with AppSource visuals → AppSource visuals are often slower 🐌📊
D. More refreshes → Doesn’t affect report performance at all ⏱️🚫
You have a Power BI model that contains a table named Employees. The table contains the following columns:
- Employee ID
- First Name
- Last Name
- Department
- Salary
Each employee is uniquely identified by using Employee ID.
You need to create a DAX measure that will calculate the average salary of all the employees in the sales department.
Which DAX expression should you use?
A. DISTINCTCOUNT(‘Employees’[Salary])
B. CALCULATE(DISTINCTCOUNT(‘Employees’[Salary]), ‘Employees’[Department] = “Sales”)
C. CALCULATE(AVERAGE(‘Employees’[Salary]), ‘Employees’[Department] = “Sales”)
D. AVERAGE(‘Employees’[Salary])
✔ Correct Answer: C.
CALCULATE(AVERAGE(‘Employees’[Salary]), ‘Employees’[Department] = “Sales”) 💰📊
Why:
You need the average salary, but only for Sales.
CALCULATE applies the “Sales” filter → then computes the AVERAGE. 🎯
❌ Why not the others?
A → Counts distinct salaries, not useful 🤦♀️
B → Same: DISTINCTCOUNT = wrong calculation ❌
D → Averages all departments, not just Sales 🌍🚫
You have a Power BI data model that contains a table named Employees. The table has the following columns:
- Employee Name
- Email Address
- Start Date
- Job Title
You are implementing dynamic row-level security (RLS).
You need to create a table filter to meet the following requirements:
- Users must see only their own employee data.
- The DAX expression must work in both Power BI Desktop and the Power BI service.
Which expression should you use?
A. [Email Address] - USERNAME()
B. [Employee Name] - USERPRINCIPALNAME()
C. [Employee Name] = USERNAME()
D. [Email Address] = USERPRINCIPALNAME()
✔ Correct Answer: D. [Email Address] = USERPRINCIPALNAME() 📧🔐✨
Why:
Dynamic RLS normally matches email → not display names.
USERPRINCIPALNAME() returns the user’s email address consistently in both Desktop and Service. ✔️🌐
Works perfectly for row-level filtering based on each user’s identity.
❌ Why not the others?
A Uses a dash instead of comparison → invalid DAX ❌
B & C Use Employee Name, which doesn’t match login identity 👤🚫
USERNAME() returns different values between Desktop and Service → inconsistent 💻⚠️☁️
You create a Power BI report named Summary1.
You discover that Summary1 is slow.
You run Performance analyzer to identify performance metrics for Summary1.
Which two metrics display the execution duration in Performance analyzer? Each correct answer present part of the solution.
NOTE: Each correct answer is worth one point.
A. Top Operations
B. DAX query
C. Server requests
D. Dependencies
E. Visual display
✔ Correct Answers: B and E ⏱️📊⚡
B. DAX query
E. Visual display
Why these two?
DAX query → Shows how long the data engine takes to run the DAX for the visual ⏱️🧠
Visual display → Shows how long it takes Power BI to render the visual on screen 🎨📈
Both are shown directly in Performance Analyzer with execution duration.
❌ Why not the others?
A. Top Operations → Not a Performance Analyzer metric list 🚫
C. Server requests → Only lists queries; doesn’t show full execution time ❌
D. Dependencies → Not a timing metric 🧩🚫
You have a Power BI model that contains two tables named Sales and Date. The Sales table relates to the Date table by using a many-to-one relationship. The Sales table contains the following columns:
- Date
- Product
- SalesAmount
You need to create a DAX measure for a rolling 31-day sales total that will return the total sales amount for a selected date and the previous 30 days.
Which DAX expression should you use?
A. CALCULATE(SUM(Sales[SalesAmount]), DATEADD(Date[Date], -30, DAY))
B. CALCULATE(SUM(Sales[SalesAmount]), DATESBETWEEN(Date[Date], Max(‘Date’[Date])-30, Max(‘Date’[Date])))
C. CALCULATE(SUM(Sales[SalesAmount]), DATESMTD(Date[Date]))
D. CALCULATE(SUM(Sales[SalesAmount]), DISTINCTCOUNT(Date[Date]) = 31)
✔ Correct Answer: B.
CALCULATE(SUM(Sales[SalesAmount]), DATESBETWEEN(Date[Date], MAX(Date[Date]) - 30, MAX(Date[Date]))) 📅📊🔥
Why:
DATESBETWEEN lets you define a custom rolling window:
MAX(Date) → the selected date
MAX(Date) – 30 → 30 days before
Together = 31-day rolling window ✔️🗓️
❌ Why not the others?
A. DATEADD(-30) → Returns only the value 30 days before, not a rolling window ❌
C. DATESMTD → Month-to-date, not 31-day rolling ❌
D. DISTINCTCOUNT = 31 → Invalid filter logic ❌🧮
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.
You have an on-premises data gateway.
You need to reduce the amount of data sent through the gateway by semantic models that run in Import storage mode.
Solution: You create aggregations to summarize results.
Does this meet the goal?
A. Yes
B. No
❌ Correct Answer: B. No
Why?
For Import mode, the semantic model still refreshes the full detailed data through the on-premises gateway during refresh — even if you create aggregations.
Aggregations improve query performance, not gateway refresh volume.
👉 Import refresh = always pulls all underlying data through the gateway.
So:
Aggregations ≠ reduce gateway data traffic ❌
Aggregations only help during report queries, not during gateway refresh.
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.
You have an on-premises data gateway.
You need to reduce the amount of data sent through the gateway by semantic models that run in import storage mode.
Solution: You increase Automatic page refresh intervals.
Does this meet the goal?
A. Yes
B. No
✔ Correct Answer: B. No ❌⛔
Why:
Automatic page refresh controls how often visuals refresh in the report, not how much data is imported or sent through the gateway.
It does NOT reduce data volume for Import models. 🚫📦
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.
You have an on-premises data gateway.
You need to reduce the amount of data sent through the gateway by semantic models that run in Import storage mode.
Solution: You decrease the dashboard cache update frequency.
Does this meet the goal?
A. Yes
B. No
✔ Correct Answer: B. No ❌📉
Why:
Dashboard cache refresh frequency only controls how often dashboards pull cached results, not how much data is sent through the gateway during Import model refreshes.
So it does NOT reduce gateway data volume. 🚫📦
You have a Power BI semantic model named Model1 that runs in Import storage mode.
You need to reduce the size of Model1. The solution must NOT increase report query response times.
What should you do?
A. Remove unnecessary columns.
B. Unpivot unnecessary columns.
C. Rename unnecessary columns.
D. Change Model1 to DirectQuery storage mode.
✔ Correct Answer: A. Remove unnecessary columns. 🗑️📦⚡
Why:
Deleting unused columns shrinks the Import model without slowing queries. This is the safest, fastest way to reduce size while keeping performance high. 🚀
❌ Why not the others?
B. Unpivot → Makes the model bigger, not smaller ❌📈
C. Rename → No impact on size 😅
D. DirectQuery → Reduces size but hurts performance 🐢🚫
You have a Power BI model that contains a table named Expenses. The table contains the following columns:
- Date
- Amount
- Category
- Expense ID
- Employee ID
Expenses are recorded by employees, and each expense has a unique expense ID.
You need to create a DAX measure that will calculate the total amount of expenses.
Which DAX expression should you use?
A. Sum(‘Expenses’[Amount])
B. Max(‘Expenses’[Amount])
C. Sum(‘Expenses’[Expense ID])
D. Count(‘Expenses’[Amount])
✔ Correct Answer: A. SUM(‘Expenses’[Amount]) 💰➕📊
Why:
You need the total expenses, so summing the Amount column is the correct aggregation. Perfect match for the requirement. ✔️
❌ Why not the others?
B Max → Only returns the highest expense ❌
C Sum of Expense ID → Meaningless; IDs aren’t additive 🚫
D Count → Counts rows, doesn’t total money 🧮❌
You build a report to help the sales team understand its performance and the drivers of sales.
The team needs to have a single visualization to identify which factors affect success.
Which type of visualization should you use?
A. Key influencers
B. Line and clustered column chart
C. Q&A
D. Funnel chart
✔ Correct Answer: A. Key influencers 🔍📊✨
Why:
Key Influencers automatically analyzes the data to show which factors most impact success, perfect for understanding sales drivers. 💡🚀
❌ Why not the others?
B Trend + comparison, not driver analysis 📉
C Q&A natural language, not influence detection 🤖
D Funnel shows stages, not root causes 🔻
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.
You have a clustered bar chart that contains a measure named Salary as the value and a field named Employee as the axis. Salary is present in the data as a numerical amount representing US dollars.
You need to create a reference line to show which employees are above the median salary.
Solution: You create a constant line and set the value to .5.
Does this meet the goal?
A. Yes
B. No
✔ Correct Answer: B. No ❌📉
Why:
Setting a constant line to 0.5 does NOT represent the median salary — salaries are dollar amounts, not percentages.
A correct solution would calculate the median salary using a measure and use that value as the reference line. 💵📊
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.
You have a clustered bar chart that contains a measure named Salary as the value and a field named Employee as the axis. Salary is present in the data as a numerical amount representing US dollars.
You need to create a reference line to show which employees are above the median salary.
Solution: You create an average line by using the Salary measure.
Does this meet the goal?
A. Yes
B. No
✔ Correct Answer: B. No ❌📉
Why:
You need a median salary line, not an average salary line.
An average line will not correctly identify employees above the median — those are different statistical values. 📊🔢
You have a collection of reports for the HR department of your company.
You need to create a visualization for the HR department that shows historical employee counts and predicts trends during the next six months.
Which type of visualization should you use?
A. ribbon chart
B. scatter chart
C. line chart
D. key influencers
✔ Correct Answer: C. Line chart 📈🔮✨
Why:
A line chart supports:
Historical trends over time 📆
Built-in forecasting for future months 🔮
Perfect for employee count trends.
❌ Why not the others?
A. Ribbon chart → Shows category ranking changes, not forecasting 🎗️
B. Scatter chart → Relationship analysis, not time trends 🔵
D. Key influencers → Shows drivers, not forecasts 🔍
You are creating a Power BI report to analyze consumer purchasing patterns from a table named Transactions. The Transactions table contains a numeric field named Spend.
You need to include a visual that identifies which fields have the greatest impact on Spend.
Which type of visual should you use?
A. Q&A
B. smart narrative
C. decomposition tree
D. key influencers
✔ Correct Answer: D. Key influencers 🔍💸✨
Why:
The Key Influencers visual identifies which fields most impact a given metric — in this case, Spend. Perfect for analyzing what drives consumer purchasing patterns. 🎯📊
❌ Why not the others?
A. Q&A → Natural language queries, not driver analysis 💬
B. Smart narrative → Summarizes insights, doesn’t find influencers 📝
C. Decomposition tree → Breakdown exploration, not driver ranking 🌳
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.
You have a clustered bar chart that contains a measure named Salary as the value and a field named Employee as the axis. Salary is present in the data as a numerical amount representing US dollars.
You need to create a reference line to show which employees are above the median salary.
Solution: You create a median line by using the Salary measure.
Does this meet the goal?
A. Yes
B. No
✔ Correct Answer: A. Yes ✅📊✨
Why:
Creating a median line using the Salary measure does exactly what you need — it adds a reference line at the median Salary value, allowing you to see which employees’ bars rise above it.
That meets the requirement perfectly. 🎯
You need to create a visual that compares profit across 10 product categories for a selected quarter.
What is the best visual to use to achieve the goal?
A. an area chart
B. a funnel chart
C. a clustered bar chart
D. a line chart
✔ Correct Answer: C. A clustered bar chart 📊🔥
Why:
You need to compare profit across 10 categories in a selected quarter.
A clustered bar chart is perfect for clear side-by-side category comparison — best readability for many categories. ✔️📈
❌ Why not the others?
A. Area chart → For trends, not category comparison 📉
B. Funnel chart → For process stages, not categories 🔻
D. Line chart → For time trends, not categorical comparisons ⏱️
You have a Power BI dataset named Finance that is hosted in a Power BI workspace.
The finance team at your company is NOT currently a member of any Power BI workspace roles.
You need to enable the finance team to use Microsoft Excel to analyze the Finance dataset.
What should you do?
A. Grant the finance team build permissions to the Finance dataset.
B. Provide an Excel workbook that is connected to the Finance dataset.
C. Create a row-level security (RLS) role and add the finance team to the role as members.
D. Grant the finance team write permissions to the Finance dataset.
✔ Correct Answer: A. Grant the finance team Build permissions. 🔗📊✨
Why:
To analyze a Power BI dataset in Excel using:
Analyze in Excel
PivotTables connected to the dataset
…users must have Build permission on the dataset.
This is the minimum required permission — perfect for least privilege. 🔐✔️
❌ Why not the others?
B. Provide an Excel workbook → They still can’t refresh or interact without Build ❌
C. RLS role → Controls row access, not Excel connectivity 🔒
D. Write permission → Too much permission (can edit/overwrite) 🚫💥
You have a Power BI report that contains a card. The card displays the value for year-to-date revenue.
You need to ensure that screen reader users can read the value when initially interacting with the card. The value must stay updated as the dataset is refreshed.
What should you do?
A. Convert the card into a text box.
B. Add the value to the chart title text.
C. Populate the alt text with a static value.
D. Populate the alt text by using conditional formatting with a DAX measure.
✔ Correct Answer: D. Populate the alt text using conditional formatting with a DAX measure. 🔊📊✨
Why:
Screen readers read alt text, not the visual itself.
Using conditional formatting with a DAX measure lets the alt text always display the current YTD revenue value, updating automatically with data refresh. Perfect accessibility solution. ✔️♿
❌ Why not the others?
A. Convert to text box → Text boxes don’t update dynamically ❌
B. Add value to title → Screen readers won’t reliably read chart titles 🚫
C. Static alt text → Will NOT update with refresh ❌
From Power BI Desktop, you publish a new dataset and report to a Power BI workspace. The dataset has a row-level security (RLS) role named HR.
You need to ensure that the HR team members have RLS applied when they view reports based on the dataset.
What should you do?
A. From powerbi.com, add users to the HR role for the dataset.
B. From powerbi.com, share the dataset to the HR team members.
C. From Power BI Desktop, change the Row-Level Security settings.
D. From Power BI Desktop, import a table that contains the HR team members.
✔ Correct Answer: A. Add users to the HR role in powerbi.com. 🔐👥✨
Why:
After publishing, RLS only works when users are assigned to roles in the Power BI Service.
Adding the HR team to the HR role ensures they see only their allowed data when viewing reports. ✔️
❌ Why not the others?
B. Share the dataset → Sharing doesn’t apply RLS ❌
C. Change RLS settings in Desktop → RLS must be assigned after publishing 🔄
D. Importing a table of HR members → Not how role assignments work 🚫
You have a Power BI dashboard that monitors the quality of manufacturing processes. The dashboard contains the following elements:
✑ A line chart that shows the number of defective products manufactured by day
✑ A KPI visual that shows the current daily percentage of defective products manufactured
You need to be notified when the daily percentage of defective products manufactured exceeds 3%.
What should you create?
A. a subscription
B. an alert
C. a smart narrative visual
D. a Q&A visual
✔ Correct Answer: B. An alert 🚨📊✨
Why:
Dashboard alerts notify you when a KPI or card hits a threshold — like “% defects > 3%”.
Perfect for real-time monitoring + email notifications. ✔️
❌ Why not the others?
A. Subscription → Sends scheduled report emails, NOT threshold alerts ❌
C. Smart narrative → Text summary, no notifications 📝
D. Q&A → Natural language query, not monitoring 🤖
You create a report by using Microsoft Power BI Desktop.
The report uses data from a Microsoft SQL Server Analysis Services (SSAS) cube located on your company’s internal network.
You plan to publish the report to the Power BI Service.
What should you implement to ensure that users who consume the report from the Power BI Service have the most up-to-date data from the cube?
A. an OData feed
B. an On-premises data gateway
C. a subscription
D. a scheduled refresh of the dataset
✔ Correct Answer: B. On-premises data gateway 🔌🏢✨
Why:
SSAS is on-premises, and Power BI Service cannot connect to it directly.
An on-premises data gateway is required so the live connection can query the SSAS cube and show up-to-date data to users in the service. ⚡📊
❌ Why not the others?
A. OData feed → Not used for SSAS cubes ❌
C. Subscription → Sends report emails, doesn’t refresh data ❌
D. Scheduled refresh → Not supported for SSAS live connections 🚫
You publish a dataset to the Power BI service. The dataset contains a connection to an on-premises Microsoft SQL Server database.
You attempt to configure a scheduled refresh but cannot select the appropriate on-premises data gateway.
You confirm the following with the administrator of the gateway:
- You have the appropriate permissions to use the gateway.
- The data source was created on the gateway.
- The gateway has a status of Running.
What is the most likely reason the gateway is unavailable?
A. The type of data source is not supported by the on-premises data gateway.
B. The server name in the PBIX file does not match the data source name in the gateway. Most Voted
C. The credentials for the data source are invalid.
D. The data source is configured to use single sign-on (SSO).
✔ Correct Answer: B. The server name in the PBIX does not match the gateway data source. 🔍🔗❌
Why:
For Power BI to recognize and use a gateway, the server name + database name in your PBIX must exactly match the data source settings in the gateway.
Even tiny differences (uppercase, instance name, spacing, using IP vs hostname) will make the gateway not appear. 🛑
❌ Why not the others?
A. Unsupported source → SQL Server is supported ✔️
C. Invalid credentials → Would cause auth errors, but the gateway would still appear 🚫
D. SSO enabled → Doesn’t hide the gateway ✔️
