2025/November Latest Braindump2go DP-600 Exam Dumps with PDF and VCE Free Updated Today! Following are some new Braindump2go DP-600 Real Exam Questions!
QUESTION 1
Case Study 1 – Contoso
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 Productine2ws.
– 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.
You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.
What should you do?
A. Store at the semantic models and reports in Data Lake Gen2 storage.
B. Modify the settings of the Research workspaces to use a GitHub repository.
C. Modify the settings of the Research division workspaces to use an Azure Repos repository.
D. Store all the semantic models and reports in Microsoft OneDrive.
Answer: C
Explanation:
Currently, only Git in Azure Repos is supported.
https://learn.microsoft.com/en-us/fabric/cicd/git-integration/intro-to-git-integration#considerations-and-limitations
QUESTION 2
Case Study 1 – Contoso
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 Productine2ws.
– 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.
You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements.
What should you include in the solution?
A. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse
B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderID column in the destination lakehouse
C. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderID column in the destination lakehouse
D. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse
Answer: D
Explanation:
We need to retrieve the maximum OrderID in the destination table to minimize the number of rows added during refresh. this would be an incremental load. can be done with data flows.
QUESTION 3
Case Study 1 – Contoso
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 Productine2ws.
– 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/productline1/ResearchProduct”)
B. spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct “)
C. external_table(‘Tables/ResearchProduct)
D. external_table(ResearchProduct)
Answer: B
Explanation:
The syntax of C and D is correct for KQL databases (incorrect in this use-case). When the shortcut is created, no additional folders have been added to the Tables section, therefore answer A is incorrect. Once created, the line of answer B can be used to access data correctly.
https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts
QUESTION 4
Case Study 1 – Contoso
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 Productine2ws.
– 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.
Hotspot Question
You need to recommend a solution to group the Research division workspaces.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
https://learn.microsoft.com/en-us/fabric/governance/domains#configure-domain-settings
QUESTION 5
Case Study 2 – Litware, Inc
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.
What should you recommend using to ingest the customer data into the data store in the AnalyticsPOC workspace?
A. a stored procedure
B. a pipeline that contains a KQL activity
C. a Spark notebook
D. a dataflow
Answer: D
Explanation:
Even though the text reads “Data will be loaded without transformation in one area of the AnalyticsPOC data store”: in general, dataflows are used when data transformations are involved after ingestion. As suggested by user BHARAT, the Copy Activity should be the optimal solution.
QUESTION 6
Case Study 2 – Litware, Inc
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.
Which type of data store should you recommend in the AnalyticsPOC workspace?
A. a data lake
B. a warehouse
C. a lakehouse
D. an external Hive metastore
Answer: C
Explanation:
The data store must handle semi-structured and unstructured data, therefore a Lakehouse should be the optimal solution supporting read access with T-SQL and Python.
QUESTION 7
Case Study 2 – Litware, Inc
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.
Hotspot Question
You need to assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 8
Case Study 2 – Litware, Inc
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.
Hotspot Question
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 9
Case Study 2 – Litware, Inc
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.
Hotspot Question
You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 11
You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:
Table1: A Delta table created by using a shortcut
Table2: An external table created by using Spark
Table3: A managed table
You plan to connect to Lakehouse1 by using its SQL endpoint.
What will you be able to do after connecting to Lakehouse1?
A. Read Table3.
B. Update the data Table3.
C. Read Table2.
D. Update the data in Table1.
Answer: A
QUESTION 12
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a PowerQuery step to identify the maximum values for the numeric columns.
Which function should you include in the step?
A. Table.MaxN
B. Table.Max
C. Table.Range
D. Table.Profile
Answer: D
Explanation:
https://learn.microsoft.com/en-us/powerquery-m/table-profile
QUESTION 13
You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace.
You need to use the model to generate predictions by using the PREDICT function in a Fabric notebook.
Which two languages can you use to perform model scoring? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
A. T-SQL
B. DAX
C. Spark SQL
D. PySpark
Answer: CD
Explanation:
https://learn.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-score-model-predict-spark-pool
QUESTION 14
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?
A. displayHTML
B. show
C. write
D. display
Answer: D
QUESTION 15
You have a Fabric tenant that contains a Microsoft Power BI report named Report1. Report1 includes a Python visual.
Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed.
You need all rows to appear in the visual.
What should you do?
A. Reference the columns in the Python code by index.
B. Modify the Sort Column By property for all columns.
C. Add a unique field to each row.
D. Modify the Summarize By property for all columns.
Answer: D
Explanation:
By setting the “Summarize By” property to “None” for all columns, you disable automatic aggregation and ensure all rows, including duplicates, are displayed in the Python visual.
QUESTION 16
You have a Fabric workspace named Workspace1 that contains a dataflow named Dataflow1. Dataflow1 has a query that returns 2,000 rows.
You view the query in Power Query as shown in the following exhibit.
What can you identify about the pickupLongitude column?
A. The column has duplicate values.
B. All the table rows are profiled.
C. The column has missing values.
D. There are 935 values that occur only once.
Answer: A
QUESTION 17
You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dataset named DS1.
You need to ensure read-write access to DS1 is available by using XMLA endpoint.
What should be modified first?
A. the DS1 settings
B. the WS1 settings
C. the C1 settings
D. the Tenant1 settings
Answer: C
Explanation:
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-connect-tools
QUESTION 18
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 is assigned to a Fabric capacity.
You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.
Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled.
B. From the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled.
C. From the Tenant settings, select Users can edit data model in the Power BI service.
D. From the Capacity settings, set XMLA Endpoint to Read Write.
E. From the Tenant settings, set Users can create Fabric items to Enabled.
F. From the Tenant settings, enable Publish to Web.
Answer: ACD
QUESTION 19
You are creating a semantic model in Microsoft Power BI Desktop.
You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL) extension for Microsoft Visual Studio Code.
You need to save the semantic model to a file.
Which file format should you use?
A. PBIP
B. PBIX
C. PBIT
D. PBIDS
Answer: A
Explanation:
The PBIP will create one file and two folders, PBIP.Dataset contains definition folder that is use to host the .tmdl files.
QUESTION 20
You plan to deploy Microsoft Power BI items by using Fabric deployment pipelines. You have a deployment pipeline that contains three stages named Development, Test, and Production. A workspace is assigned to each stage.
You need to provide Power BI developers with access to the pipeline. The solution must meet the following requirements:
– Ensure that the developers can deploy items to the workspaces for Development and Test.
– Prevent the developers from deploying items to the workspace for Production.
– Follow the principle of least privilege.
Which three levels of access should you assign to the developers? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Build permission to the production semantic models
B. Admin access to the deployment pipeline
C. Viewer access to the Development and Test workspaces
D. Viewer access to the Production workspace
E. Contributor access to the Development and Test workspaces
F. Contributor access to the Production workspace
Answer: ADE
QUESTION 21
You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bi report named Report1 that uses the model. Report1 contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution,
NOTE: Each correct answer is worth one point.
A. user-defined aggregations
B. automatic aggregation
C. query caching
D. OneLake integration
Answer: AB
QUESTION 22
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power BI semantic model named Model1 that uses the CSV files as a data source. You configure incremental refresh for Model1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?
A. Query folding is occurring.
B. Only refresh complete days is selected.
C. XMLA Endpoint is set to Read Only.
D. Query folding is NOT occurring.
E. The delta type of the column used to partition the data has changed.
Answer: D
Explanation:
https://learn.microsoft.com/en-us/power-bi/connect-data/incremental-refresh-troubleshoot#problem-loading-data-takes-too-long
QUESTION 23
You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?
A. At the semantic model level, set Large dataset storage format to Off.
B. At the tenant level, set Create and use Metrics to Enabled.
C. At the semantic model level, set Large dataset storage format to On.
D. At the tenant level, set Data Activator to Enabled.
Answer: C
Explanation:
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-scale-out-configure
QUESTION 24
You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS).
You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse.
When users interact with a report built from the model, which mode will be used by the DAX queries?
A. DirectQuery
B. Dual
C. Direct Lake
D. Import
Answer: A
Explanation:
Row-level security only applies to queries on a Warehouse or SQL analytics endpoint in Fabric. Power BI queries on a warehouse in Direct Lake mode will fall back to Direct Query mode to abide by row-level security.
https://learn.microsoft.com/en-us/fabric/data-warehouse/row-level-security
QUESTION 25
You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales.
You need to create a diagram of the model. The diagram must contain only the Sales table and related tables.
What should you use from Microsoft Power BI Desktop?
A. data categories
B. Data view
C. Model view
D. DAX query view
Answer: C
Explanation:
In the Model view, it is possible to analyze the semantic model and create new layouts.
QUESTION 26
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
A. Use the Analyze in Excel feature.
B. Use the Vertipaq Analyzer tool.
C. Query the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS dynamic management view (DMV).
D. Query the DISCOVER_MEMORYGRANT dynamic management view (DMV).
Answer: BC
QUESTION 27
You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields.
You need to reduce the memory used by Model1 and the time it takes to refresh the model.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Split OrderDateTime into separate date and time columns.
B. Replace TotalQuantity with a calculated column.
C. Convert Quantity into the Text data type.
D. Replace TotalSalesAmount with a measure.
Answer: AD
QUESTION 28
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
A. Microsoft Power BI Desktop
B. Tabular Editor
C. Microsoft SQL Server Management Studio (SSMS)
D. DAX Studio
Answer: AB
Explanation:
To prevent report creators from populating visuals using implicit measures in a Power BI semantic model within a Fabric tenant, you can utilize the following tools:
1. Tabular Editor:
2. Power BI Desktop (Data Model View):
QUESTION 29
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Tablet.
You are creating a new data pipeline.
You plan to copy external data to Table1. The schema of the external data changes regularly.
You need the copy operation to meet the following requirements:
– Replace Table1 with the schema of the external data.
– Replace all the data in Table1 with the rows in the external data.
You add a Copy data activity to the pipeline.
What should you do for the Copy data activity?
A. From the Source tab, add additional columns.
B. From the Destination tab, set Table action to Overwrite.
C. From the Settings tab, select Enable staging.
D. From the Source tab, select Enable partition discovery.
E. From the Source tab, select Recursively.
Answer: B
Explanation:
Enable “Truncate table” option: This option truncates the target table before copying data, ensuring that all existing data is replaced with the new data from the external source.
QUESTION 30
You have a Fabric tenant that contains a lakehouse.
You plan to query sales data files by using the SQL endpoint. The files will be in an Amazon Simple Storage Service (Amazon S3) storage bucket.
You need to recommend which file format to use and where to create a shortcut.
Which two actions should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Create a shortcut in the Files section.
B. Use the Parquet format
C. Use the CSV format.
D. Create a shortcut in the Tables section.
E. Use the delta format.
Answer: BD
Explanation:
You should use a columnar file format such as Parquet or ORC (Optimized Row Columnar). These formats are highly optimized for analytical queries and provide efficient storage and query performance.
In the Tables section of your lakehouse, you define virtual tables that represent external data sources. These virtual tables can be backed by data stored externally in formats such as Parquet or ORC in Amazon S3.
QUESTION 31
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files.
You need to convert the CSV files into the delta format that has V-Order optimization enabled.
What should you do from Lakehouse explorer?
A. Use the Load to Tables feature.
B. Create a new shortcut in the Files section.
C. Create a new shortcut in the Tables section.
D. Use the Optimize feature.
Answer: A
Explanation:
With ”Load to tables” : tables are always loaded using the Delta Lake table format with V-Order optimization enabled.
https://learn.microsoft.com/en-us/fabric/data-engineering/load-to-tables#load-to-table-capabilities-overview
QUESTION 32
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?
A. From the Destination tab, set Mode to Append.
B. From the Destination tab, select the partition column.
C. From the Source tab, select Enable partition discovery.
D. From the Destination tabs, set Mode to Overwrite.
Answer: D
Explanation:
When setting up the Copy Activity, you need to choose the Overwrite mode to make the partition option appear (not visibile in Append mode).
QUESTION 33
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
Support the use of dataflows to load and append data to the data store.
Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?
A. a lakehouse
B. an Azure SQL database
C. a warehouse
D. a KQL database
Answer: A
Explanation:
To meet the requirements of supporting dataflows to load and append data to the data store while ensuring that Delta tables are V-Order optimized and compacted automatically, you should use a lakehouse in Fabric as your solution.
QUESTION 34
You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1 contains a query that returns the data shown in the following exhibit.
You need to transform the data columns into attribute-value pairs, where columns become rows.
You select the VendorID column.
Which transformation should you select from the context menu of the VendorID column?
A. Group by
B. Unpivot columns
C. Unpivot other columns
D. Split column
E. Remove other columns
Answer: C
QUESTION 35
You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?
A. Daily
B. By the minute
C. Weekly
D. Hourly
Answer: C
Explanation:
The only way to do this is to set the schedule to ”Weekly”, set the days on Monday and Friday and add manually 6 Time of 4 hour intervals.
QUESTION 36
You have a Fabric tenant that contains a warehouse.
Several times a day, the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.
What should you use to identify whether throttling is occurring?
A. the Capacity settings
B. the Monitoring hub
C. dynamic management views (DMVs)
D. the Microsoft Fabric Capacity Metrics app
Answer: D
QUESTION 37
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?
A. sys.dm_exec_requests
B. sys.dm_exec_sessions
C. sys.dm_exec_connections
D. sys.dm_pdw_exec_requests
Answer: A
Explanation:
https://learn.microsoft.com/en-us/fabric/data-warehouse/monitor-using-dmv
QUESTION 38
You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
A. Update rows when the non-key attributes have changed.
B. Insert new rows when the natural key exists in the dimension table, and the non-key attribute values have changed.
C. Update the effective end date of rows when the non-key attribute values have changed.
D. Insert new records when the natural key is a new value in the table.
Answer: AD
Explanation:
Type 1 SCD does not preserve history, therefore no end dates for table entries exists.
QUESTION 39
You are analyzing customer purchases in a Fabric notebook by using PySpark.
You have the following DataFrames:
– transactions: Contains five columns named transaction_id, customer_id, product_id, amount, and date and has 10 million rows, with each row representing a transaction.
– customers: Contains customer details in 1,000 rows and three columns named customer_id, name, and country.
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling.
You write the following code.
from pyspark.sql import functions as F
results =
Which code should you run to populate the results DataFrame?
A. transactions.join(F.broadcast(customers), transactions.customer_id == customers.customer_id)
B. transactions.join(customers, transactions.customer_id == customers.customer_id).distinct()
C. transactions.join(customers, transactions.customer_id == customers.customer_id)
D. transactions.crossJoin(customers).where(transactions.customer_id == customers.customer_id)
Answer: A
Explanation:
https://sparkbyexamples.com/spark/broadcast-join-in-spark/”
QUESTION 40
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()
Does this meet the goal?
A. Yes
B. No
Answer: B
QUESTION 41
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.show()
Does this meet the goal?
A. Yes
B. No
Answer: B
QUESTION 42
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.summary()
Does this meet the goal?
A. Yes
B. No
Answer: A
QUESTION 43
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 HISTORY customer
Does this meet the goal?
A. Yes
B. No
Answer: A
QUESTION 44
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: REFRESH TABLE customer
Does this meet the goal?
A. Yes
B. No
Answer: B
QUESTION 45
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: EXPLAIN TABLE customer
Does this meet the goal?
A. Yes
B. No
Answer: B
QUESTION 46
Hotspot Question
You have a data warehouse that contains a table named Stage.Customers. Stage.Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer.
You need to write a T-SQL query that will return the customer ID, name. postal code, and the last updated time of the most recent row for each customer ID.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 47
Hotspot Question
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power BI visuals.
You run the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 48
Drag and Drop Question
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint. The query must return a table of stores that have opened since December 1, 2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets. Each value 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.
Answer:
QUESTION 49
Hotspot Question
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains three schemas named schemaA, schemaB, and schemaC.
You need to ensure that a user named User1 can truncate tables in schemaA only.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 50
Hotspot Question
You have the source data model shown in the following exhibit.
The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 51
Hotspot Question
You have a Fabric tenant that contains two lakehouses.
You are building a dataflow that will combine data from the lakehouses. The applied steps from one of the queries in the dataflow is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
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