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Question 1/101/10
Topic 1, Contoso, Ltd
Case Study
Overview
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to
Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company’s IT department has a team of data analysts and a team of data engineers that use
analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use
Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write
queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license
mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure
Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a
private virtual network that has public access blocked. POS1 contains all the sales transactions that
were processed on the company’s website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven
entities. The entities contain data that relates to email open rates and interaction rates, as well as
website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a
different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an
Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from
300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
You need to ensure that the data analysts can access the gold layer lakehouse.
What should you do?
Select the answer:Select the answer
1 correct answerA.
Add the DataAnalyst group to the Viewer role for WorkspaceA.
B.
Share the lakehouse with the DataAnalysts group and grant the Build reports on the default semantic model permission.
C.
Share the lakehouse with the DataAnalysts group and grant the Read all SQL Endpoint data permission.
D.
Share the lakehouse with the DataAnalysts group and grant the Read all Apache Spark permission.
Quiz
Question 2/102/10
Topic 1, Contoso, Ltd
Case Study
Overview
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to
Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company’s IT department has a team of data analysts and a team of data engineers that use
analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use
Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write
queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license
mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure
Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a
private virtual network that has public access blocked. POS1 contains all the sales transactions that
were processed on the company’s website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven
entities. The entities contain data that relates to email open rates and interaction rates, as well as
website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a
different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an
Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from
300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
HOTSPOT
You need to recommend a method to populate the POS1 data to the lakehouse medallion layers.
What should you recommend for each layer? To answer, select the appropriate options in the answer
area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Bronze Layer: A pipeline Copy activity
The bronze layer is used to store raw, unprocessed dat
a. The requirements specify that no transformations should be applied before landing the data in this
layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and
the ability to ingest the data directly into the Delta format in the bronze layer.
Silver Layer: A notebook
The silver layer involves extensive data cleansing (deduplication, handling missing values, and
standardizing capitalization). A notebook provides the flexibility to implement complex
transformations and is well-suited for this task.Quiz
Question 3/103/10
Topic 1, Contoso, Ltd
Case Study
Overview
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to
Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company’s IT department has a team of data analysts and a team of data engineers that use
analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use
Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write
queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license
mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure
Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a
private virtual network that has public access blocked. POS1 contains all the sales transactions that
were processed on the company’s website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven
entities. The entities contain data that relates to email open rates and interaction rates, as well as
website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a
different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an
Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from
300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical
requirements.
What should you do?
Select the answer:Select the answer
1 correct answerA.
Create a workspace identity and enable high concurrency for the notebooks.
B.
Create a shortcut and ensure that caching is disabled for the workspace.
C.
Create a workspace identity and use the identity in a data pipeline.
D.
Create a shortcut and ensure that caching is enabled for the workspace.
does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to
the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress
costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw
data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly
from the Amazon S3 bucket.Quiz
Question 4/104/10
Topic 1, Contoso, Ltd
Case Study
Overview
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to
Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company’s IT department has a team of data analysts and a team of data engineers that use
analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use
Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write
queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license
mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure
Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a
private virtual network that has public access blocked. POS1 contains all the sales transactions that
were processed on the company’s website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven
entities. The entities contain data that relates to email open rates and interaction rates, as well as
website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a
different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an
Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from
300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
HOTSPOT
You need to create the product dimension.
How should you complete the Apache Spark SQL code? To answer, select the appropriate options in
the answer area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Join between Products and ProductSubCategories:
Use an INNER JOIN.
The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that
only matching records (i.e., products with a valid subcategory) are included.
Join between ProductSubCategories and ProductCategories:
Use an INNER JOIN.
Similar to the above logic, we want to include only subcategories assigned to a valid product
category. An INNER JOIN ensures this condition is met.
WHERE Clause
Condition: IsActive = 1
Only active products (where IsActive equals 1) should be included in the gold layer. This filters out
inactive products.Quiz
Question 5/105/10
Topic 1, Contoso, Ltd
Case Study
Overview
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to
Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company’s IT department has a team of data analysts and a team of data engineers that use
analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use
Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write
queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license
mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure
Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a
private virtual network that has public access blocked. POS1 contains all the sales transactions that
were processed on the company’s website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven
entities. The entities contain data that relates to email open rates and interaction rates, as well as
website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a
different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an
Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from
300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
Products
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
ProductCategories
ProductSubcategories
In the data, products are related to product subcategories, and subcategories are related to product
categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories
that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email
content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce
the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences
transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze,
silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the
silver layer, including deduplication, the handling of missing values, and the standardizing of
capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the
lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in
the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the
source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models,
reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must
include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT
analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
The data engineers must have read and write access to all the lakehouses, including the underlying
files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
You need to populate the MAR1 data in the bronze layer.
Which two types of activities should you include in the pipeline? Each correct answer presents part
of the solution.
NOTE: Each correct selection is worth one point.
Select multiple answer: (Choose 2)Select the answer
2 correct answersA.
ForEach
B.
Copy data
C.
WebHook
D.
Stored procedure
Quiz
Question 6/106/10
Topic 2, Litware, Inc
Case Study
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you
would like to complete each case. However, there may be additional case studies and sections on
this exam. You must manage your time to ensure that you are able to complete all questions included
on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is
provided in the case study. Case studies might contain exhibits and other resources that provide
more information about the scenario that is described in the case study. Each question is
independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your
answers and to make changes before you move to the next section of the exam. After you begin a
new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane
to explore the content of the case study before you answer the questions. Clicking these buttons
displays information such as business requirements, existing environment, and problem statements.
If the case study has an All Information tab, note that the information displayed is identical to the
information displayed on the subsequent tabs. When you are ready to answer a question, click the
Question button to return to the question.
Overview
Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores
worldwide. Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore
constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and
gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a
lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers.
The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than
one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical
data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
You need to implement the solution for the book reviews.
Which should you do?
Select the answer:Select the answer
1 correct answerA.
Create a Dataflow Gen2 dataflow.
B.
Create a shortcut.
C.
Enable external data sharing.
D.
Create a data pipeline.
Quiz
Question 7/107/10
Topic 2, Litware, Inc
Case Study
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you
would like to complete each case. However, there may be additional case studies and sections on
this exam. You must manage your time to ensure that you are able to complete all questions included
on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is
provided in the case study. Case studies might contain exhibits and other resources that provide
more information about the scenario that is described in the case study. Each question is
independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your
answers and to make changes before you move to the next section of the exam. After you begin a
new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane
to explore the content of the case study before you answer the questions. Clicking these buttons
displays information such as business requirements, existing environment, and problem statements.
If the case study has an All Information tab, note that the information displayed is identical to the
information displayed on the subsequent tabs. When you are ready to answer a question, click the
Question button to return to the question.
Overview
Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores
worldwide. Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore
constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and
gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a
lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers.
The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than
one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical
data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
You need to resolve the sales data issue. The solution must minimize the amount of data transferred.
What should you do?
Select the answer:Select the answer
1 correct answerA.
Spilt the dataflow into two dataflows.
B.
Configure scheduled refresh for the dataflow.
C.
Configure incremental refresh for the dataflow. Set Store rows from the past to 1 Month.
D.
Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Year.
E.
Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Month.
Quiz
Question 8/108/10
Topic 2, Litware, Inc
Case Study
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you
would like to complete each case. However, there may be additional case studies and sections on
this exam. You must manage your time to ensure that you are able to complete all questions included
on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is
provided in the case study. Case studies might contain exhibits and other resources that provide
more information about the scenario that is described in the case study. Each question is
independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your
answers and to make changes before you move to the next section of the exam. After you begin a
new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane
to explore the content of the case study before you answer the questions. Clicking these buttons
displays information such as business requirements, existing environment, and problem statements.
If the case study has an All Information tab, note that the information displayed is identical to the
information displayed on the subsequent tabs. When you are ready to answer a question, click the
Question button to return to the question.
Overview
Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores
worldwide. Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore
constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and
gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a
lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers.
The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than
one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical
data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a
column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email
address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that
reports against the warehouse sometimes run for two hours and fail to load as expected. Upon
further investigation, the data engineering team receives the following error message when the
reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one
failure.
When the authors have new book releases, there is often an increase in sales activity. This increase
slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-
date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the
data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be
streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and
follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional
Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.
HOTSPOT
You need to troubleshoot the ad-hoc query issue.
How should you complete the statement? To answer, select the appropriate options in the answer
area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
SELECT last_run_start_time, last_run_command: These fields will help identify the execution details
of the long-running queries.
FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries
using the queryinsights.long_running_queries view, which provides insights into queries that take
longer than expected to execute.
WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more
than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.
AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more
than once, helping to isolate the problematic queries that cause failures and need attention.Quiz
Question 9/109/10
You have a Fabric workspace.
You have semi-structured data.
You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by
using Spark.
What should you use to store the data?
Select the answer:Select the answer
1 correct answerA.
a lakehouse
B.
an eventhouse
C.
a datamart
D.
a warehouse
Quiz
Question 10/1010/10
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using
an on-premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?
Select the answer:Select the answer
1 correct answerA.
a Dataflow Gen1 dataflow
B.
a data pipeline
C.
a KQL queryset
D.
a notebook
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