You have an Azure Data Lake Storage Gen2 account named storage1 that contains a Parquet file named sales.parquet.
You have a Fabric tenant containing a workspace named Workspace1.
Using a notebook in Workspace1, you need to load the file’s content into the default lakehouse. The solution must ensure the content automatically appears as a table named Sales in Lakehouse explorer.
You have a Fabric tenant containing a workspace named Enterprise. Enterprise contains a semantic model named Model1, which has a Power Query date parameter named Date1.
You build a deployment pipeline named Enterprise Data with two stages: Development and Test. You assign the Enterprise workspace to Development.
You need to perform these actions:
Create a workspace named Enterprise [Test] and assign it to the Test stage.
Configure a rule that modifies Date1’s value when changes deploy to the Test stage.
Which two settings should you use?
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You have a Fabric tenant that includes a semantic model named Model1. Model1 includes a fact table named FactSales. FactSales has a relationship with a dimension table named DimDate through a column named OrderDate.
You add a second relationship to FactSales between FactSales and DimDate, based on a column named ShippedDate.
You need to filter by using ShippedDate in a new report. The solution must not affect how existing reports function.
Which two actions should you take? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
Choose two
AEnable the ShippedDate relationship.
BUse a RELATEDTABLE DAX function.
CUse a CROSSFILTER DAX function.
DUse a USERELATIONSHIP DAX function.
EDisable the ShippedDate relationship between the tables.
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You have a Fabric tenant containing four workspaces named Development, Test, QA, and Production. All the workspaces use Premium Per User (PPU) license mode.
You plan to use a release pipeline to support the development lifecycle from Development to Production.
Which three actions should you perform, in sequence?
Drag & Drop
Move each workspace to Pro license mode.
Create a deployment pipeline.
Create a deployment rule.
Assign workspaces.
Create the QA stage.
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You have a Fabric tenant that includes a complex semantic model. The model uses a star schema and includes many tables, including a fact table named Sales.
You need to display a diagram of the model. The diagram must include only the Sales table and its related tables.
What should you use in Microsoft Power BI Desktop?
Adata categories
BData view
CModel view
DDAX query view
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You have a Fabric workspace named Workspace1.
Workspace1 contains multiple semantic models, including a model named Model1. Model1 is updated by using an XMLA endpoint.
You need to increase the speed of the write operations of the XMLA endpoint.
What should you do?
ADelete any unused semantic models from Workspace1.
BSelect Large semantic model storage format for Workspace1.
CConfigure Model 1 to use the Direct Lake storage format.
DDelete any unused columns from Model1.
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You have a Fabric tenant that contains the workspaces shown in the following table.
You have a deployment pipeline named Pipeline1 that deploys items from Workspace_DEV to Workspace_TEST. In Pipeline1, all items that have matching names are paired.
You deploy the contents of Workspace_DEV to Workspace_TEST by using Pipeline1.
What will the contents of Workspace_TEST be once the deployment is complete?
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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 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.
What should you use to implement calculation groups for the Research division semantic models?
AMicrosoft Power BI Desktop
Bthe Power BI service
CDAX Studio
DTabular Editor
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You have a Fabric warehouse containing a table named Sales.Products. Sales.Products contains the following columns.
You need to write a T-SQL query that returns the following columns.
How should you complete the code?
Select
SELECT ProductID,
(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice,
(ListPrice, WholesalePrice, AgentPrice) AS TradePrice
FROM Sales.Products;
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You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?
AAppend variable
BScript
CStored procedure
DGet metadata
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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?
ADaily
BBy the minute
CWeekly
DHourly
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Save question
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?
AQuery folding is occurring.
BOnly refresh complete days is selected.
CXMLA Endpoint is set to Read Only.
DQuery folding is NOT occurring.
EThe delta type of the column used to partition the data has changed.
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HOTSPOT
You have a data warehouse containing the following tables:
DimCustomer
DimEmployee
DimGeography
FactInternetSales
Every table has a primary key and these relationships:
CustomerKey = FactInternetSales.CustomerKey
EmployeeKey = FactInternetSales.EmployeeKey
Geography = DimCustomer.GeographyKey
You have a T-SQL query named Query1 that contains the following statements.
For each statement below, select Yes when the statement is true. Otherwise, select No.
Yes or No
Statements
Yes
No
Query1 will return multiple rows for a single customer if the customer has placed more than one online sales order.
If a customer record in DimCustomer has a GeographyKey value that does NOT exist in DimGeography, the customer’s sales data will still appear in the Query1 results.
Adding the clause GROUP BY g.EnglishCountryRegionName to Query1 will calculate the total sales amount by country.
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You have a Fabric workspace containing a large warehouse.
You plan to create a lakehouse named Lakehouse1 for a sales dataset. Lakehouse1 will contain these tables:
Sales: Contains sales transactions
Stores: Contains a unique list of store names and locations
Loyalty: Contains a list of customers and their preferred stores
Customers: Contains a unique list of customer names and addresses
Products: Contains a unique list of available products and their descriptions
You need to configure a star schema for Lakehouse1.
Which table should be defined as the fact table, and which relationship type should be configured from the Sales table to the Customers table?
Select
Fact table:
Relationship:
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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.
Choose three
AFrom the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled.
BFrom the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled.
CFrom the Tenant settings, select Users can edit data model in the Power BI service.
DFrom the Capacity settings, set XMLA Endpoint to Read Write.
EFrom the Tenant settings, set Users can create Fabric items to Enabled.
FFrom the Tenant settings, enable Publish to Web.
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Save question
You have the following KQL query.
For each statement, select Yes if it is true. Otherwise, select No.
Yes or No
Statements
Yes
No
The query excludes sales that have a Status of Cancelled.
The query calculates the total sales of each product category for the last 30 days.
The query includes product categories that have had zero sales during the last 30 days.
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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?
Athe DS1 settings
Bthe WS1 settings
Cthe C1 settings
Dthe Tenant1 settings
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You have a semantic model named Model1 that contains data that relates to customers and their bank account balances.
Model1 has the following tables and columns.
A customer can have one or more accounts. Each account can be associated to multiple customers.
You need to ensure that users can query Model1 to identify the total transaction amounts by customer.
What should you add to Model1?
Aa many-to-many relationship between FactTransaction and Dim Customer
Ba bridge table with relationships to DimCustomer and DimAccount
Ca bridge table with relationships to FactTransaction and DimCustomer
Dthe CustomerKey column in FactTransaction and a relationship to DimCustomer
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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?
AGroup by
BUnpivot columns
CUnpivot other columns
DSplit column
ERemove other columns
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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 create a DAX measure that calculates the average overall satisfaction score.
How should you complete the DAX code?
Select
Rolling 12 Overall Satisfaction =
VAR NumberOfMonths = 12
VAR LastCurrentDate = MAX ( 'Date' [Date] )
VAR Period = DATESINPERIOD ( 'Date' [Date], LastCurrentDate,
- NumberOfMonths, MONTH )
VAR Result =
CALCULATE (
'Survey Question'[Question Title] = “Overall Satisfaction”
)
RETURN
Result
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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 resolve the pricing-group classification issue.
How should you complete the T-SQL statement?
Select
CREATE [dbo].[ProductsWithPricingGroup]
AS
SELECT ProductId,
ProductName,
ProductCategory,
ListPrice,
WHEN ListPrice <= 50 THEN ‘low’
WHEN ListPrice > 1000 THEN ‘high’
END AS PricingGroup
FROM dbo.Products
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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 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?
Select
DataEngineers:
DataAnalysts:
DataScientists:
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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 design a semantic model for the customer satisfaction report.
Which data-source authentication method and mode should you use?
Select
Authentication method:
Mode:
0
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