Quiz Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.