2025/November Latest Braindump2go DP-700 Exam Dumps with PDF and VCE Free Updated Today! Following are some new Braindump2go DP-700 Real Exam Questions!
QUESTION 1
Case Study 1 – Contoso, Ltd
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.
You need to ensure that the data analysts can access the gold layer lakehouse.
What should you do?
A. 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.
Answer: C
Explanation:
Data Analysts’ Access Requirements must only have read access to the Delta tables in the gold layer and not have access to the bronze and silver layers.
The gold layer data is typically queried via SQL Endpoints. Granting the Read all SQL Endpoint data permission allows data analysts to query the data using familiar SQL-based tools while restricting access to the underlying files.
QUESTION 2
Case Study 1 – Contoso, Ltd
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.
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?
A. 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.
Answer: D
Explanation:
Enabling caching for the workspace will help minimize egress costs by reducing the amount of data that needs to be transferred across clouds. Creating a shortcut ensures that the raw data is not duplicated in the lakehouse.
QUESTION 3
Case Study 1 – Contoso, Ltd
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.
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.
A. ForEach
B. Copy data
C. WebHook
D. Stored procedure
Answer: AB
Explanation:
MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required to iterate over these endpoints to fetch data from each one. It enables dynamic execution of API calls for each entity.
The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the bronze layer in Delta format. It supports native connectors for REST APIs and Delta, minimizing development effort.
QUESTION 4
Case Study 1 – Contoso, Ltd
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.
Hotspot Question
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.
Answer:
Explanation:
Bronze Layer: A pipeline Copy activity
The bronze layer is used to store raw, unprocessed data. 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.
QUESTION 5
Case Study 1 – Contoso, Ltd
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.
Hotspot Question
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.
Answer:
Explanation:
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.
QUESTION 6
Case Study 2 – Litware, Inc
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.
You need to implement the solution for the book reviews.
Which should you do?
A. Create a Dataflow Gen2 dataflow.
B. Create a shortcut.
C. Enable external data sharing.
D. Create a data pipeline.
Answer: B
Explanation:
The requirement specifies that Litware plans to make the book reviews available in the lakehouse without making a copy of the data. In this case, creating a shortcut in Fabric is the most appropriate solution. A shortcut is a reference to the external data, and it allows Litware to access the book reviews stored in Amazon S3 without duplicating the data into the lakehouse.
QUESTION 7
Case Study 2 – Litware, Inc
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.
You need to resolve the sales data issue. The solution must minimize the amount of data transferred.
What should you do?
A. 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.
Answer: E
Explanation:
The sales data issue can be resolved by configuring incremental refresh for the dataflow. Incremental refresh allows for only the new or changed data to be processed, minimizing the amount of data transferred and improving performance.
The solution specifies that data older than one month never changes, so setting the refresh period to 1 Month is appropriate. This ensures that only the most recent month of data will be refreshed, reducing unnecessary data transfers.
QUESTION 8
Case Study 2 – Litware, Inc
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.
Hotspot Question
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.
Answer:
Explanation:
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.
QUESTION 9
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?
A. a lakehouse
B. an eventhouse
C. a datamart
D. a warehouse
Answer: B
Explanation:
Eventhouse:
Read operations: KQL, Spark and T-SQL
Write operations: KQL, Spark
https://learn.microsoft.com/en-us/fabric/get-started/decision-guide-data-store
QUESTION 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?
A. a Dataflow Gen1 dataflow
B. a data pipeline
C. a KQL queryset
D. a notebook
Answer: B
Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Microsoft Fabric, the best option is to use a data pipeline. A data pipeline in Fabric allows for the orchestration of data movement, from source to destination, using connectors, transformations, and scheduled workflows. Since the data is being transferred from an on-premises database and requires the use of a data gateway, a data pipeline provides the appropriate framework to facilitate this data movement efficiently and reliably.
QUESTION 11
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?
A. an Apache Spark job definition
B. a data pipeline
C. a Dataflow Gen1 dataflow
D. an eventstream
Answer: B
Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Fabric, a data pipeline is the most appropriate tool. A data pipeline in Fabric is designed to move data between various data sources and destinations, including on-premises databases like SQL Server, and cloud-based storage like Fabric warehouses. The data pipeline can handle the connection through an on-premises data gateway, which is required to access on- premises data. This solution facilitates the orchestration of data movement and transformations if needed.
QUESTION 12
You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys. DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.
You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.
Users report that the performance of some of the reports has degraded over time and some visuals show errors.
You need to resolve the performance issues.
The solution must meet the following requirements:
– Provide the best query performance.
– Minimize operational costs.
Which should you do?
A. Change the MD5 hash to SHA256.
B. Increase the capacity.
C. Enable V-Order.
D. Modify the surrogate keys to use a different data type.
E. Create views.
Answer: B
Explanation:
* Increase the capacity.
Direct Lake in Fabric F32 Capacity supports tables up to 300 million rows. F64 can handle 500 million rows.
Reference:
https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/Fabric-Capacity-Scaling-and-Power-BI-What-happens-when-Power-BI/ba-p/4403792
QUESTION 13
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.
You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Date
B. ProductName
C. ProductColor
D. TransactionID
E. SalesAmount
F. ProductID
Answer: BCF
Explanation:
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It’s essential for uniquely identifying each product in the model.
QUESTION 14
You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1. In Workspace1, you create a new notebook named Notebook2. You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?
A. Enable high concurrency for notebooks.
B. Enable dynamic allocation for the Spark pool.
C. Change the runtime version.
D. Increase the number of executors.
Answer: A
Explanation:
To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.
QUESTION 15
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
Lakehouse1 contains the following tables:
– Orders
– Customer
– Employee
The Employee table contains Personally Identifiable Information (PII). A data engineer is building a workflow that requires writing data to the Customer table, however, the user does NOT have the elevated permissions required to view the contents of the Employee table. You need to ensure that the data engineer can write data to the Customer table without reading data from the Employee table.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Share Lakehouse1 with the data engineer.
B. Assign the data engineer the Contributor role for Workspace2.
C. Assign the data engineer the Viewer role for Workspace2.
D. Assign the data engineer the Contributor role for Workspace1.
E. Migrate the Employee table from Lakehouse1 to Lakehouse2.
F. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2.
G. Assign the data engineer the Viewer role for Workspace1.
Answer: DEF
Explanation:
Assign the data engineer the Contributor role for Workspace1 ( D)
– This will provide the necessary permissions to write to the Customer table.
Migrate the Employee table from Lakehouse1 to Lakehouse2 (E)
– This will isolate the table with PII in a different lakehouse.
Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2 (Option F)
– This is necessary to separate the Employee table into a different workspace to restrict access.
QUESTION 16
You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives.
You plan to implement row-level security (RLS).
You need to ensure that the sales representatives can see only their respective data. Which warehouse object do you require to implement RLS?
A. ISTORED PROCEDURE
B. CONSTRAINT
C. SCHEMA
D. FUNCTION
Answer: D
Explanation:
To implement Row-Level Security (RLS) in a Fabric warehouse, you need to use a function that defines the security logic for filtering the rows of data based on the user’s identity or role. This function can be used in conjunction with a security policy to control access to specific rows in a table.
In the case of sales representatives, the function would define the filtering criteria (e.g., based on a column such as SalesRepID or SalesRepName), ensuring that each representative can only see their respective data.
QUESTION 17
You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.
You need to deploy an eventhouse as part of the deployment process.
What should you use to add the eventhouse to the deployment process?
A. GitHub Actions
B. a deployment pipeline
C. an Azure DevOps pipeline
Answer: B
Explanation:
A deployment pipeline in Fabric is designed to automate the process of deploying assets (such as reports, datasets, eventhouses, and other objects) between environments like Dev, Test, and Prod. Since you need to deploy an eventhouse as part of the deployment process, a deployment pipeline is the appropriate tool to move this asset through the different stages of your environment.
QUESTION 18
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1. You plan to deploy Warehouse1 to a new workspace named Workspace2. As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort.
What should you use?
A. a database project
B. a deployment pipeline
C. a Python script
D. a T-SQL script
Answer: B
Explanation:
Microsoft Fabric’s deployment pipelines provide a built-in mechanism to manage and validate the deployment of artifacts like warehouses. When you use a deployment pipeline to move Warehouse1 from one workspace (Workspace1) to another (Workspace2), the pipeline automatically checks for issues such as invalid references or missing dependencies during the deployment process.
QUESTION 19
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse. Users report that from OneLake file explorer, they cannot see the data from the eventhouse.
You enable OneLake availability for the eventhouse.
What will be copied to OneLake?
A. only data added to new databases that are added to the eventhouse
B. only the existing data in the eventhouse
C. no data
D. both new data and existing data in the eventhouse
E. only new data added to the eventhouse
Answer: E
Explanation:
– Existing tables aren’t affected. New tables are made available in OneLake.
https://learn.microsoft.com/en-us/fabric/real-time-intelligence/event-house-onelake-availability#how-it-works
QUESTION 20
You have a Fabric workspace named Workspace1.
You plan to integrate Workspace1 with Azure DevOps.
You will use a Fabric deployment pipeline named deployPipeline1 to deploy items from Workspace1 to higher environment workspaces as part of a medallion architecture. You will run deployPipeline1 by using an API call from an Azure DevOps pipeline.
You need to configure API authentication between Azure DevOps and Fabric.
Which type of authentication should you use?
A. service principal
B. Microsoft Entra username and password
C. managed private endpoint
D. workspace identity
Answer: A
Explanation:
When integrating Azure DevOps with Fabric (Workspace1), using a service principal is the recommended authentication method. A service principal provides a way for applications (such as an Azure DevOps pipeline) to authenticate and interact with resources securely. It allows Azure DevOps to authenticate API calls to Fabric without requiring direct user credentials. This method is ideal for automating tasks such as deploying items through a Fabric deployment pipeline.
QUESTION 21
You have a Google Cloud Storage (GCS) container named storage1 that contains the files shown in the following table.
You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the shortcuts shown in the following table.
You need to read data from all the shortcuts.
Which shortcuts will retrieve data from the cache?
A. Stores only
B. Products only
C. Stores and Products only
D. Products, Stores, and Trips
E. Trips only
F. Products and Trips only
Answer: C
Explanation:
When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).
Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to 12 hours, it is likely that this data will be retrieved from the cache, as it hasn’t been too long since it was last accessed.
Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period. Therefore, this data will also be retrieved from the cache.
Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it’s outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.
QUESTION 22
You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1.
You have an Azure SQL database named Source1 that has public internet access disabled. You need to ensure that Job1 can access the data in Source1.
What should you create?
A. an on-premises data gateway
B. a managed private endpoint
C. an integration runtime
D. a data management gateway
Answer: B
Explanation:
To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access disabled, you need to create a managed private endpoint. A managed private endpoint is a secure, private connection that enables services like Fabric (or other Azure services) to access resources such as databases, storage accounts, or other services within a virtual network (VNet) without requiring public internet access. This approach maintains the security and integrity of your data while enabling access to the Azure SQL database.
QUESTION 23
You have an Azure Data Lake Storage Gen2 account named storage1 and an Amazon S3 bucket named storage2.
You have the Delta Parquet files shown in the following table.
You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the following shortcuts:
– A shortcut to ProductFile aliased as Products
– A shortcut to StoreFile aliased as Stores
– A shortcut to TripsFile aliased as Trips
The data from which shortcuts will be retrieved from the cache?
A. Trips and Stores only
B. Products and Store only
C. Stores only
D. Products only
E. Products. Stores, and Trips
Answer: C
Explanation:
Shortcut caching is currently only supported for GCS, S3 and S3 compatible shortcuts.
If a file hasn’t been accessed for more than 24 hrs it’s purged from the cache. Individual files greater than 1 GB in size aren’t cached.
https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts#caching
QUESTION 24
Your company has a sales department that uses two Fabric workspaces named Workspace1 and Workspace2.
The company decides to implement a domain strategy to organize the workspaces. You need to ensure that a user can perform the following tasks:
– Create a new domain for the sales department.
– Create two subdomains: one for the east region and one for the west region.
– Assign Workspace1 to the east region subdomain.
– Assign Workspace2 to the west region subdomain.
The solution must follow the principle of least privilege.
Which role should you assign to the user?
A. workspace Admin
B. domain admin
C. domain contributor
D. Fabric admin
Answer: D
Explanation:
Only a Fabric Admin can create Domains.
https://learn.microsoft.com/en-us/fabric/governance/domains?source=recommendations
QUESTION 25
You have a Fabric workspace named Workspace1 that contains a warehouse named DW1 and a data pipeline named Pipeline1.
You plan to add a user named User3 to Workspace1.
You need to ensure that User3 can perform the following actions:
– View all the items in Workspace1.
– Update the tables in DW1.
The solution must follow the principle of least privilege.
You already assigned the appropriate object-level permissions to DW1.
Which workspace role should you assign to User3?
A. Admin
B. Member
C. Viewer
D. Contributor
Answer: D
Explanation:
Viewer – Can view all content in the workspace, but can’t modify it.
Contributor – Can view and modify all content in the workspace.
Member – Can view, modify, and share all content in the workspace. Can add Members
Admin – Can view, modify, share, and manage all content in the workspace, including managing permissions.
– Can add Admins, Members and can delete workspace.
So Contributor is the least role who can view and update the tables (modify the content).
QUESTION 26
You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports.
A user named User1 wants to use SQL to analyze the data in Lakehouse1.
You need to configure access for User1. The solution must meet the following requirements:
– Provide User1 with read access to the table data in Lakehouse1.
– Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1.
– Prevent User1 from accessing other items in Workspace1.
What should you do?
A. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.
B. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
C. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.
D. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
Answer: A
Explanation:
Assigning the Viewer role would grant User1 access to all items in the workspace, violating the requirement to restrict access to Lakehouse1 only.
QUESTION 27
You have two Fabric workspaces named Workspace1 and Workspace2.
You have a Fabric deployment pipeline named deployPipeline1 that deploys items from Workspace1 to Workspace2. DeployPipeline1 contains all the items in Workspace1.
You recently modified the items in Workspaces1.
The workspaces currently contain the items shown in the following table.
Items in Workspace1 that have the same name as items in Workspace2 are currently paired.
You need to ensure that the items in Workspace1 overwrite the corresponding items in Workspace2.
The solution must minimize effort.
What should you do?
A. Delete all the items in Workspace2, and then run deployPipeline1.
B. Rename each item in Workspace2 to have the same name as the items in Workspace1.
C. Back up the items in Workspace2, and then run deployPipeline1.
D. Run deployPipeline1 without modifying the items in Workspace2.
Answer: D
Explanation:
When running a deployment pipeline in Fabric, if the items in Workspace1 are paired with the corresponding items in Workspace2 (based on the same name), the deployment pipeline will automatically overwrite the existing items in Workspace2 with the modified items from Workspace1. There’s no need to delete, rename, or back up items manually unless you need to keep versions. By simply running deployPipeline1, the pipeline will handle overwriting the existing items in Workspace2 based on the pairing, ensuring the latest version of the items is deployed with minimal effort.
QUESTION 28
You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.
You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2. You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1. You use deployPipeline1 to deploy Workspace1 to Workspace2.
What occurs to Workspace2?
A. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed.
B. Only Pipeline1 and Lakehouse1 are deployed.
C. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1.
D. Only Folder1 is created and Pipeline1 moves to Folder1.
Answer: A
Explanation:
When content from the source stage is copied to the target stage, Fabric identifies existing content in the target stage and overwrites it.
https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/understand-the-deployment-process?tabs=new-ui#deploy-content-from-one-stage-to-another
QUESTION 29
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic. The data from the DataFrame is then written to a new Delta table named Table2 by using a merge operation.
You need to consolidate the underlying Parquet files in Table1.
Which command should you run?
A. VACUUM
B. BROADCAST
C. OPTIMIZE
D. CACHE
Answer: C
Explanation:
To consolidate the underlying Parquet files in Table1 and improve query performance by optimizing the data layout, you should use the OPTIMIZE command in Delta Lake. The OPTIMIZE command coalesces smaller files into larger ones and reorganizes the data for more efficient reads. This is particularly useful when working with large datasets in Delta tables, as it helps reduce the number of files and improves performance for subsequent queries or operations like MERGE.
QUESTION 30
You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub. You need to identify in which workspace a specific item runs.
Which column should you view in Monitoring hub?
A. Start time
B. Capacity
C. Activity name
D. Submitter
E. Item type
F. Job type
G. Location
Answer: G
Explanation:
The Location shows the Workspace.
https://learn.microsoft.com/en-us/training/modules/monitor-fabric-items/3-use-monitor-hub
QUESTION 31
You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements
– Trigger the process when a new file is added.
– Provide the highest throughput.
Which type of item should you use to ingest the data?
A. Event stream
B. Dataflow Gen2
C. Streaming dataset
D. Data pipeline
Answer: D
Explanation:
Eventstream is designed for ingesting real-time or streaming data from sources like IoT devices or logs. It’s not optimized for batch processing or large files.
QUESTION 32
You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements:
– Trigger the process when a new file is added.
– Provide the highest throughput.
Which type of item should you use to ingest the data?
A. Data pipeline
B. Environment
C. KQL queryset
D. Dataflow Gen2
Answer: A
Explanation:
For high-throughput, event-triggered ingestion of large files into a lakehouse without transformations, Data pipeline is the most appropriate and efficient item in Fabric. added.
QUESTION 34
You are developing a data pipeline named Pipeline1.
You need to add a Copy data activity that will copy data from a Snowflake data source to a Fabric warehouse.
What should you configure?
A. Degree of copy parallelism
B. Fault tolerance
C. Enable staging
D. Enable logging
Answer: C
Explanation:
When using the Copy data activity in a data pipeline to move data from Snowflake to a Fabric warehouse, the process often involves intermediate staging to handle data efficiently, especially for large datasets or cross-cloud data transfers.
Staging involves temporarily storing data in an intermediate location (e.g., Blob storage or Azure
Data Lake) before loading it into the target destination. For cross-cloud data transfers (e.g., from Snowflake to Fabric), enabling staging ensures data is processed and stored temporarily in an efficient format for transfer. Staging is especially useful when dealing with large datasets, ensuring the process is optimized and avoids memory limitations.
QUESTION 35
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You change the join type to kind=outer.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time. It does not improve query performance.
QUESTION 36
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You change project to extend.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Using extend retains all columns in the table, potentially increasing the size of the output unnecessarily. project is more efficient because it selects only the required columns.
QUESTION 37
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You move the filter to line 02.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Moving the filter to line 02: Filtering the Stream table before performing the join operation reduces the number of rows that need to be processed during the join. This is an effective optimization technique for queries involving large datasets.
QUESTION 38
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You add the make_list() function to the output columns.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.
QUESTION 39
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.
You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each. You need to minimize how long it takes to query Table1.
What should you do?
A. Disable V-Order and run the OPTIMIZE command.
B. Disable V-Order and run the VACUUM command.
C. Run the OPTIMIZE and VACUUM commands.
Answer: C
Explanation:
OPTIMIZE: This will help compact the small Parquet files into larger files, improving the efficiency of queries. VACUUM: This will remove old versions of files that are no longer needed, freeing up storage and further optimizing performance..
QUESTION 40
You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded daily into Warehouse1 by using data pipelines and stored procedures. You discover that the daily data load takes longer than expected. You need to monitor Warehouse1 to identify the names of users that are actively running queries.
Which view should you use?
A. sys.dm_exec_connections
B. sys.dm_exec_requests
C. queryinsights.long_running_queries
D. queryinsights.frequently_run_queries
E. sys.dm_exec_sessions
Answer: E
Explanation:
sys.dm_exec_sessions provides real-time information about all active sessions, including the user, session ID, and status of the session. You can filter on session status to see users actively running queries.
QUESTION 41
Hotspot Question
You have a Fabric workspace named Workspace1_DEV that contains the following items:
– 10 reports
– Four notebooks
– Three lakehouses
– Two data pipelines
– Two Dataflow Gen1 dataflows
– Three Dataflow Gen2 dataflows
– Five semantic models that each has a scheduled refresh policy
You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST.
You deploy all the items from Workspace1_DEV to Workspace1_TEST.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 42
Hotspot Question
You have a Fabric workspace named Workspace1 that contains the items shown in the following table.
For Model1, the Keep your Direct Lake data up to date option is disabled.
You need to configure the execution of the items to meet the following requirements:
– Notebook1 must execute every weekday at 8:00 AM.
– Notebook2 must execute when a file is saved to an Azure Blob Storage container.
– Model1 must refresh when Notebook1 has executed successfully.
How should you orchestrate each item? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 43
Drag and Drop Question
You are implementing the following data entities in a Fabric environment:
– Entity1: Available in a lakehouse and contains data that will be used as a core organization entity
– Entity2: Available in a semantic model and contains data that meets organizational standards
– Entity3: Available in a Microsoft Power BI report and contains data that is ready for sharing and reuse
– Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making
Your company requires that specific governance processes be implemented for the data.
You need to apply endorsement badges to the entities based on each entity’s use case.
Which badge should you apply to each entity? To answer, drag the appropriate badges the correct entities. Each badge may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 44
Hotspot Question
You have three users named User1, User2, and User3.
You have the Fabric workspaces shown in the following table.
You have a security group named Group1 that contains User1 and User3.
The Fabric admin creates the domains shown in the following table.
User1 creates a new workspace named Workspace3.
You add Group1 to the default domain of Domain1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Being a domain contributor doesn’t give you any permissions on the workspace itself, only on the domain. Workspace roles are set differently. This excludes the first and last option. User 3 will be domain contributor on the new workspace. Even though without domain admin permissions, there’s nothing user 3 can do with Workspace 3.
https://learn.microsoft.com/en-us/fabric/governance/domains
QUESTION 45
Drag and Drop Question
Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data.
You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks.
You need to ensure that the libraries are available by default to new notebooks in Workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
QUESTION 46
Drag and Drop Question
You have a Fabric workspace that contains a warehouse named Warehouse1.
In Warehouse1, you create a table named DimCustomer by running the following statement.
You need to set the Customerkey column as a primary key of the DimCustomer table.
Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
Answer:
Explanation:
PRIMARY KEY is only supported when NONCLUSTERED and NOT ENFORCED are both used.
https://learn.microsoft.com/en-us/fabric/data-warehouse/table-constraints
QUESTION 47
Hotspot Question
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains the following tables and columns.
You need to denormalize the tables and include the ContractType and StartDate columns in the Employee table. The solution must meet the following requirements:
– Ensure that the StartDate column is of the date data type.
– Ensure that all the rows from the Employee table are preserved and include any matching rows from the Contract table.
– Ensure that the result set displays the total number of employees per contract type for all the contract types that have more than two employees.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 48
Hotspot Question
You have an Azure Event Hubs data source that contains weather data.
You ingest the data from the data source by using an eventstream named Eventstream1.
Eventstream1 uses a lakehouse as the destination.
You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort.
What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 49
Hotspot Question
You are building a data loading pattern for Fabric notebook workloads.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 50
Hotspot Question
You have a Fabric workspace that contains two lakehouses named Lakehouse1 and Lakehouse2. Lakehouse1 contains staging data in a Delta table named Orderlines. Lakehouse2 contains a Type 2 slowly changing dimension (SCD) dimension table named Dim_Customer.
You need to build a query that will combine data from Orderlines and Dim_Customer to create a new fact table named Fact_Orders. The new table must meet the following requirements:
– Enable the analysis of customer orders based on historical attributes.
– Enable the analysis of customer orders based on the current attributes.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 51
Hotspot Question
You have a Fabric workspace.
You are debugging a statement and discover the following issues:
– Sometimes, the statement fails to return all the expected rows.
– The PurchaseDate output column is NOT in the expected format of mmm dd, yy.
You need to resolve the issues. The solution must ensure that the data types of the results are retained. The results can contain blank cells.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 52
Drag and Drop Question
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData. The following is a sample of the data in TaxiData.
You need to build two KQL queries. The solution must meet the following requirements:
– One of the queries must partition RunningTotalAmount by VendorID.
– The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Partition the RunningTotalAmount by VendorID. – Row_cumsum The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).
Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type – Row_window_session
QUESTION 53
Hotspot Question
You are processing streaming data from an external data provider.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 54
Hotspot Question
You have a Fabric workspace that contains an eventstream named EventStream1.
You discover that an EventStream1 transformation fails.
You need to find the following error information:
– The error details, including the occurrence time
– The total number of errors
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
QUESTION 55
You have implemented synchronous mirroring for your data warehouse. You are experiencing performance issues during peak workloads.
Which of the following techniques can help improve performance ?
A. Increase the number of dataflow activities.
B. Reduce the frequency of mirroring updates.
C. Use a faster network connection between the primary and secondary databases.
D. Increase the number of workers in the dataflow cluster
Answer: C
QUESTION 56
You need to create a pipeline to ingest data from a variety of sources, including SQL Server databases, CSV files, and JSON files.
Which of the following pipeline design strategies would be most effective?
A. Create a separate pipeline for each data source.
B. Create a single pipeline with multiple activities for each data source.
C. Create a pipeline with a lookup activity to determine the data source.
D. Create a pipeline with a script activity to dynamically create activities for each data source.
Answer: B
QUESTION 57
You need to implement mirroring for a large dataset that is frequently updated.
Which mirroring strategy would be most suitable for this scenario, considering factors like performance, availability, and cost?
A. Full mirroring
B. Transactional mirroring
C. Snapshot mirroring
D. Log-based mirroring
Answer: B
QUESTION 58
You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.
You need to identify which version of Delta was used when Notebook1 was executed.
What should you use?
A. Real-Time hub
B. OneLake data hub
C. the Admin monitoring workspace
D. Fabric Monitor
E. the Microsoft Fabric Capacity Metrics app
Answer: D
Explanation:
You can see this in the Monitor. On the Details section for a Notebook there is Runtime information which gives the version i.e. Runtime 1.3 (Spark 3.5, Delta 3.2).
QUESTION 59
You have a Fabric workspace that contains a semantic model named Model1. You need to dynamically execute and monitor the refresh progress of Model1.
What should you use?
A. dynamic management views in Microsoft SQL Server Management Studio
B. Monitoring hub
C. dynamic management views in Azure Data Studio
D. a semantic link in a notebook
Answer: D
Explanation:
If the definition of “dynamic” is the ability to execute and monitor in real-time, a semantic link in a notebook could be more suitable than the monitoring hub.
QUESTION 60
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database.
The table contains the following columns:
– BikepointID
– Street
– Neighbourhood
– No_Bikes
– No_Empty_Docks
– Timestamp
You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
Solution: You use the following code segment:
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
The “sort by” is sorting values in descending order (default behavior –> https://learn.microsoft.com/en-us/kusto/query/sort-operator?view=microsoft-fabric). One should add “asc” to sort values as required. The double “project” at the end does not affect the final result.
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