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 -
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Existing Environment -
Identity Environment -
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.
Data Environment -
Contoso has the following data environment:
• The Sales division uses a Microsoft Power BI Premium capacity.
• The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
• The Research department uses an on-premises, third-party data warehousing product.
• Fabric is enabled for contoso.com.
• An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
• A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Requirements -
Planned Changes -
Contoso plans to make the following changes:
• Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
• Make all the data for the Sales division and the Research division available in Fabric.
• For the Research division, create two Fabric workspaces named Productline1ws and Productline2ws.
• In Productline1ws, create a lakehouse named Lakehouse1.
• In Lakehouse1, create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements -
Contoso identifies the following data analytics requirements:
• All the workspaces for the Sales division and the Research division must support all Fabric experiences.
• The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
• The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
• For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
• For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
• All the semantic models and reports for the Research division must use version control that supports branching.
Data Preparation Requirements -
Contoso identifies the following data preparation requirements:
• The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
• All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements -
Contoso identifies the following requirements for implementing and managing semantic models:
• The number of rows added to the Orders table during refreshes must be minimized.
• The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements -
Contoso identifies the following high-level requirements that must be considered for all solutions:
• Follow the principle of least privilege when applicable.
• Minimize implementation and maintenance effort when possible.
Which syntax should you use in a notebook to access the Research division data for Productline1?
A. spark.read.format(“delta”).load(“Tables/ResearchProduct”)
B. spark.read.format(“delta”).load(“Files/ResearchProduct”)
C. external_table(‘Tables/ResearchProduct)
D. external_table(ResearchProduct)
Show Answer
Correct Answer: A
Explanation: The shortcut points to Delta-formatted data. To satisfy the requirement that lakehouse data be presented as managed tables in Lakehouse explorer and be accessible via Fabric notebooks, the shortcut should be created in the Tables section as a table shortcut. In Spark, Delta table shortcuts in the Tables area are loaded with spark.read.format("delta").load("Tables/ResearchProduct"). The Files path would apply to a file shortcut rather than a table shortcut, and the external_table options are not the appropriate notebook syntax.
Question 103
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 semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
ISEMPTY ( RELATEDTABLE ( 'Order Item' ) )
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: No. Replacing the condition with ISEMPTY(RELATEDTABLE('Order Item')) changes the semantics to test for the absence of related rows. If the original logic was checking for the existence of related order items (for example, COUNTROWS(...) > 0), the equivalent optimized pattern is NOT ISEMPTY(RELATEDTABLE(...)). The proposed replacement therefore does not meet the goal because it reverses the condition.
Question 104
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 semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
CALCULATE ( COUNTROWS ( 'Order Item' ) ) >= 0
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: Replacing the condition with CALCULATE(COUNTROWS('Order Item')) >= 0 does not reduce query execution time. Because COUNTROWS is never negative, the predicate is effectively always true (including zero rows), changing the logic rather than providing a meaningful performance optimization. Therefore, the proposed change does not meet the stated goal.
Question 105
You have a Fabric tenant that contains a warehouse.
You are designing a star schema model that will contain a customer dimension. The customer dimension table will be a Type 2 slowly changing dimension (SCD).
You need to recommend which columns to add to the table. The columns must NOT already exist in the source.
Which three types of columns should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. a foreign key
B. a natural key
C. an effective end date and time
D. a surrogate key
E. an effective start date and time
Show Answer
Correct Answer: C, D, E
Explanation: A Type 2 slowly changing dimension requires new rows for changed dimension members, so it adds a surrogate key to uniquely identify each version and effective start/end date-time columns to track the validity period of each version. A natural key typically already exists in the source system, and foreign keys belong on fact tables referencing the dimension rather than being added as new SCD2 tracking columns.
Question 106
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: No. `df.explain()` displays the Spark execution plan for a DataFrame and does not compute descriptive statistics. To calculate summary metrics such as min, max, mean, and standard deviation, you would use methods like `df.describe()` or `df.summary()`, then call `.show()` to display the results.
Question 107
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 about a Delta table (such as format, size, number of files, location, and properties), but it does not show the operation history. To verify whether maintenance operations such as OPTIMIZE or VACUUM were performed, you would use DESCRIBE HISTORY on the Delta table. Therefore, the proposed solution does not meet the goal.
Question 108
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 notebook reads historical churn data and creates visualizations showing the distribution of retained versus lost customers across attributes such as geography, products, age, and tenure. This summarizes and presents what has happened in the data, which is descriptive analytics. It does not identify root causes (diagnostic), forecast future churn (predictive), or recommend actions (prescriptive).
Question 109
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: Use a Fabric Data Factory pipeline with activity dependencies to orchestrate the end-to-end load sequence. Pipelines can coordinate notebooks, dataflows, and other activities, enforce that raw and cleansed layers complete before the dimensional model is populated, and be scheduled. This directly satisfies the sequencing and orchestration requirement. Dataflows, Spark notebooks, and Spark job definitions alone do not provide the same workflow orchestration with inter-activity dependencies.
Question 110
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 uses a Fabric trial capacity and only the analytics team should have access to Fabric features. Therefore, enable 'Users can try Microsoft Fabric paid features' only for specific security groups rather than the entire organization. To follow least privilege and ensure only authorized PoC participants can create Fabric artifacts, enable 'Users can create Fabric items' for specific security groups. Guest access is not required, and excluding groups is less appropriate than explicitly allowing only the required groups.
Question 111
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: A full outer join returns all rows from both tables, matching rows where possible and including nonmatching rows from either side with NULLs for missing columns. Inner, left/right outer, and anti joins do not return all rows from both tables.
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