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.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain().show()
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The goal is to calculate summary statistics (min, max, mean, standard deviation) for string and numeric columns. df.explain().show() only displays the Spark execution plan and performs no statistical analysis. To meet the goal, methods like df.describe() or df.summary() would be required.
Question 95
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.
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
DESCRIBE DETAIL customer -
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: DESCRIBE DETAIL returns metadata and current properties of a Delta table (size, files, schema, timestamps), but it does not show the history of operations. To identify whether maintenance tasks like OPTIMIZE or VACUUM were performed, you must use DESCRIBE HISTORY. Therefore, the solution does not meet the goal.
Question 96
You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.
You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age, and customer tenure.
Which type of analytics are you performing?
A. diagnostic
B. descriptive
C. prescriptive
D. predictive
Show Answer
Correct Answer: B
Explanation: The analysis reads historical churn data and visualizes distributions of retained versus lost customers across attributes such as geography, age, tenure, and number of products. This summarizes and describes what has happened using charts, without explaining root causes, predicting future churn, or recommending actions. That aligns with descriptive analytics.
Question 97
Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview -
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment -
Fabric Environment -
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data -
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
The customer satisfaction data contains the following tables:
• Survey
• Question
• Response
For each survey submitted, the following occurs:
• One row is added to the Survey table.
• One row is added to the Response table for each question in the survey.
The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems -
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements -
Planned Changes -
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
• AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
• DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
• DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
• A data store (type to be decided)
• A custom semantic model
• A default semantic model
• Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements -
The data store must support the following:
• Read access by using T-SQL or Python
• Semi-structured and unstructured data
• Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model
The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
• List prices that are less than or equal to 50 are in the low pricing group.
• List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
• List prices that are greater than 1,000 are in the high pricing group.
Security Requirements -
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
• Fabric administrators will be the workspace administrators.
• The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
• The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
• The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
• The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
• The date dimension must be available to all users of the data store.
• The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
• FabricAdmins: Fabric administrators
• AnalyticsTeam: All the members of the analytics team
• DataAnalysts: The data analysts on the analytics team
• DataScientists: The data scientists on the analytics team
• DataEngineers: The data engineers on the analytics team
• AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements -
The data analysts must create a customer satisfaction report that meets the following requirements:
• Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
• Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
• Shows data as soon as the data is updated in the data store.
• Ensures that the report and the semantic model only contain data from the current and previous year.
• Ensures that the report respects any table-level security specified in the source data store.
• Minimizes the execution time of report queries.
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do?
A. Create a dataflow that has multiple steps and schedule the dataflow.
B. Create and schedule a Spark notebook.
C. Create and schedule a Spark job definition.
D. Create a pipeline that has dependencies between activities and schedule the pipeline.
Show Answer
Correct Answer: D
Explanation: The requirement is to ensure data loading and transformation activities run in a defined sequence (raw load → cleanse/transform → dimensional model) and are scheduled. Fabric pipelines support orchestration with explicit dependencies between activities (dataflows, notebooks, Spark jobs), ensuring upstream steps complete successfully before downstream steps run. Dataflows, standalone notebooks, or Spark job definitions alone do not provide end-to-end orchestration with dependency management.
Question 98
Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview -
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment -
Fabric Environment -
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data -
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
The customer satisfaction data contains the following tables:
• Survey
• Question
• Response
For each survey submitted, the following occurs:
• One row is added to the Survey table.
• One row is added to the Response table for each question in the survey.
The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems -
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements -
Planned Changes -
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
• AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
• DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
• DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
• A data store (type to be decided)
• A custom semantic model
• A default semantic model
• Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements -
The data store must support the following:
• Read access by using T-SQL or Python
• Semi-structured and unstructured data
• Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model
The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
• List prices that are less than or equal to 50 are in the low pricing group.
• List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
• List prices that are greater than 1,000 are in the high pricing group.
Security Requirements -
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
• Fabric administrators will be the workspace administrators.
• The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
• The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
• The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
• The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
• The date dimension must be available to all users of the data store.
• The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
• FabricAdmins: Fabric administrators
• AnalyticsTeam: All the members of the analytics team
• DataAnalysts: The data analysts on the analytics team
• DataScientists: The data scientists on the analytics team
• DataEngineers: The data engineers on the analytics team
• AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements -
The data analysts must create a customer satisfaction report that meets the following requirements:
• Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
• Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
• Shows data as soon as the data is updated in the data store.
• Ensures that the report and the semantic model only contain data from the current and previous year.
• Ensures that the report respects any table-level security specified in the source data store.
• Minimizes the execution time of report queries.
You need to recommend a solution to prepare the tenant for the PoC.
Which two actions should you recommend performing from the Fabric Admin portal? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Enable the Users can try Microsoft Fabric paid features option for the entire organization.
B. Enable the Users can try Microsoft Fabric paid features option for specific security groups.
C. Enable the Allow Azure Active Directory guest users to access Microsoft Fabric option for specific security groups.
D. Enable the Users can create Fabric items option and exclude specific security groups.
E. Enable the Users can create Fabric items option for specific security groups.
Show Answer
Correct Answer: B, E
Explanation: The PoC must be limited to a subset of users and follow the principle of least privilege. Enabling Fabric paid features for specific security groups allows only the analytics team involved in the PoC to use Fabric trial capabilities, rather than the entire organization. Enabling the ability to create Fabric items for specific security groups ensures that only authorized team members can create Fabric artifacts, while preventing broader tenant users from creating items prematurely.
Question 99
You have a Fabric tenant that contains a lakehouse.
You plan to use a visual query to merge two tables.
You need to ensure that the query returns all the rows in both tables.
Which type of join should you use?
A. inner
B. full outer
C. left outer
D. right anti
E. right outer
F. left anti
Show Answer
Correct Answer: B
Explanation: To return all rows from both tables when merging, you must include matching rows as well as non-matching rows from each side. A full outer join does exactly this: it returns all records from both tables, matching where possible and filling with NULLs where there is no match. Other join types exclude rows from one or both tables.
Question 100
DRAG DROP
-
You are implementing a medallion architecture in a single Fabric workspace.
You have a lakehouse that contains the Bronze and Silver layers and a warehouse that contains the Gold layer.
You create the items required to populate the layers as shown in the following table.
You need to ensure that the layers are populated daily in sequential order such that Silver is populated only after Bronze is complete, and Gold is populated only after Silver is complete. The solution must minimize development effort and complexity.
What should you use to execute each set of items? To answer, drag the appropriate options to the correct items. Each option 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.
Show Answer
Correct Answer: Orchestration pipeline:
A schedule
Bronze layer:
A pipeline Copy activity
Silver layer:
A pipeline Dataflow activity
Gold layer:
A pipeline Stored procedure activity
Explanation: A schedule trigger runs the orchestration daily. Within the pipeline, Copy activities ingest raw data into Bronze, Dataflow activities transform data for Silver, and Stored procedure activities populate the Gold warehouse, ensuring sequential execution with minimal complexity.
Question 101
HOTSPOT
-
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.
You need to write a T-SQL query that will return the following columns.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: ChargedQuantity must return the first available value in priority order: Weight, then Quantity, otherwise 1, which is exactly the behavior of COALESCE. OrderPrice must return the lowest of ListPrice and SalePrice while ignoring NULLs in SalePrice; LEAST compares values row-by-row and ignores NULL arguments when at least one value is non-NULL.
Question 102
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
• Hide the column.
• Set Nullable to False
• Set Summarize By to None.
• Set Available in MDX to False.
• Mark the column as a key column.
What should you use?
A. Microsoft Power BI Desktop
B. ALM Toolkit
C. Tabular Editor
D. DAX Studio
Show Answer
Correct Answer: C
Explanation: The requirement is to programmatically and repeatedly change multiple column properties based on a naming pattern (columns ending with 'Key'). Tabular Editor supports C# scripting against the Tabular Object Model, allowing bulk automation of properties such as Hidden, Nullable, Summarize By, Available in MDX, and Key column designation. Power BI Desktop can only do these changes manually, ALM Toolkit is for model comparison/deployment rather than editing metadata, and DAX Studio is read/query-focused and cannot modify model structure.
Question 103
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
In Workspace1, you create a data pipeline named Pipeline1.
You have CSV files stored in an Azure Storage account.
You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1. The activity must support Power Query M formula language expressions.
Which type of activity should you add?
A. Dataflow
B. Notebook
C. Script
D. Copy data
Show Answer
Correct Answer: A
Explanation: The requirement explicitly states that the activity must support Power Query M formula language expressions. In Microsoft Fabric, Dataflows (Gen2) are built on Power Query and natively use the Power Query M language for data ingestion and transformation. A Dataflow can be added as an activity within a pipeline and can copy data from CSV files in Azure Storage into a Lakehouse. The Copy data activity focuses on straightforward data movement and does not provide full Power Query M authoring capabilities, while Notebook and Script activities do not use Power Query M.
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