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. KQL queryset
B. Streaming dataset
C. Notebook
D. Dataflow Gen2
Show Answer
Correct Answer: C
Explanation: A Notebook is the best fit because ingesting very large 500 GB files into a Fabric lakehouse with no transformations is best handled by Spark for maximum throughput. The new-file trigger can be implemented through orchestration (such as an event-triggered pipeline invoking the notebook). KQL querysets are for querying, streaming datasets are for real-time streaming scenarios, and Dataflow Gen2 is optimized for ETL/low-code ingestion rather than the highest-throughput bulk ingestion of very large files.
Question 32
DRAG DROP
-
You have a KQL database that contains a table named Readings.
You need to build a KQL query to compare the MeterReading value of each row to the previous row base on the Timestamp value.
A sample of the expected output is shown in the following table.
How should you complete the 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.
Show Answer
Correct Answer: sort
extend
project
Explanation: Sort the rows by Timestamp before using prev(). Use extend to create PrevMeterReading and PrevTimestamp with prev(), then project the required output columns. In practice, use 'sort by Timestamp asc' because sort defaults to descending.
Question 33
You have an Azure SQL database named DB1.
In a Fabric workspace, you deploy an eventstream named EventStreamDB1 to stream record changes from DB1 into a lakehouse.
You discover that events are NOT being propagated to EventStreamDB1.
You need to ensure that the events are propagated to EventStreamDB1.
What should you do?
A. Create a read-only replica of DB1.
B. Create an Azure Stream Analytics job.
C. Enable Extended Events for DB1.
D. Enable change data capture (CDC) for DB1.
Show Answer
Correct Answer: D
Explanation: Microsoft Fabric eventstreams use Change Data Capture (CDC) to capture and stream row-level changes from supported Azure SQL databases. If CDC is not enabled on the source database, change events will not be propagated. Creating a read-only replica, enabling Extended Events, or creating an Azure Stream Analytics job does not enable database change capture for this integration.
Question 34
Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview. 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 recommend a solution for handling old files. The solution must meet the technical requirements.
What should you include in the recommendation?
A. a data pipeline that includes a Copy data activity
B. a data pipeline that includes a Delete data activity
C. a notebook that runs the VACUUM command
D. a notebook that runs the OPTIMIZE command
Show Answer
Correct Answer: C
Explanation: Delta Lake's VACUUM command removes obsolete data files that are no longer referenced by the Delta transaction log. This directly satisfies the requirement to remove old files no longer referenced by a Delta table log on a scheduled basis. OPTIMIZE compacts files for performance, while Copy data and Delete data pipeline activities do not use Delta transaction log awareness and would require custom logic to identify safe-to-delete files.
Question 35
HOTSPOT
-
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Status_Target that has the following columns:
• Key
• Status
• LastModified
The data source contains a table named Status_Source that has the same columns as Status_Target. Status_Source is used to populate Status_Target.
In a notebook name Notebook1, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF.
You need to implement an incremental loading pattern by using Notebook1. The solution must meet the following requirements:
• For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
• Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
• Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status_Source.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: Use whenMatchedUpdate to update existing target rows, whenNotMatchedInsert to insert new source rows, and whenNotMatchedBySourceUpdate to update target rows missing from the source (mark inactive based on the condition).
Question 36
DRAG DROP
-
You are building a data loading pattern by using a Fabric data pipeline. The source is an Azure SQL database that contains 25 tables. The destination is a lakehouse.
In a warehouse, you create a control table named Control.Object as shown in the exhibit. (Click the Exhibit tab.)
You need to build a data pipeline that will support the dynamic ingestion of the tables listed in the control table by using a single execution.
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.
Show Answer
Correct Answer: 1. Add a Lookup activity to query Control.Object and generate a list of the schemas and tables to copy.
2. Add a ForEach activity to iterate over the list of tables.
3. Add a Copy data activity as an inner activity to the iterator activity.
Explanation: Use Lookup to read rows from the control table, ForEach to iterate through the returned array, and Copy Data inside the loop to dynamically copy each source table into the lakehouse. Get Metadata is not for querying table rows, and Until is not the appropriate iterator for a known list.
Question 37
HOTSPOT
-
You are building a data orchestration pattern by using a Fabric data pipeline named Dynamic Data Copy as shown in the exhibit. (Click the Exhibit tab.)
Dynamic Data Copy does NOT use parametrization.
You need to configure the ForEach activity to receive the list of tables to be copied.
How should you complete the pipeline expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Lookup Schema and Table
output.value
Explanation: The ForEach activity expects an array. A Lookup activity returns its result set in output.value when configured to return multiple rows, so reference the Lookup activity's output.value.
Question 38
Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview. 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 recommend a solution to resolve the MAR1 connectivity issues. The solution must minimize development effort.
What should you recommend?
A. Add a ForEach activity to the data pipeline.
B. Configure retries for the Copy data activity.
C. Call a notebook from the data pipeline.
D. Configure Fault tolerance for the Copy data activity.
Show Answer
Correct Answer: B
Explanation: Configure retries on the Copy data activity. The requirement is that transient connectivity errors should automatically attempt the connection again while minimizing development effort. The Copy data activity has built-in retry settings for transient failures. A ForEach activity is for iteration, a notebook requires additional development, and Fault tolerance is designed to handle row/file-level data issues rather than retry transient connectivity failures.
Question 39
Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview -
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.
What should you recommend that the data engineering team use to ingest the SEO data?
A. a streaming dataflow
B. a streaming dataset
C. a notebook that uses Apache Spark Structured Streaming
D. an eventstream
Show Answer
Correct Answer: D
Explanation: Eventstream is the Fabric-native service designed for near-real-time ingestion of streaming data and routing it into Fabric destinations. The requirement is to process SEO data in near real time while using only Fabric services and items. A streaming dataset is a Power BI concept rather than the recommended ingestion mechanism, a streaming dataflow is not the primary Fabric NRT ingestion service for this scenario, and a Spark Structured Streaming notebook is more appropriate for stream processing logic than as the recommended ingestion component.
Question 40
Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview -
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.
What should you do to optimize the query experience for the business users?
A. Enable V-Order.
B. Create and update statistics.
C. Run the VACUUM command.
D. Introduce primary keys.
Show Answer
Correct Answer: B
Explanation: Creating and updating statistics helps the SQL optimizer generate better execution plans for ad-hoc queries, which directly addresses long-running queries and execution failures. VACUUM is for storage cleanup, primary keys primarily enforce integrity, and V-Order cannot be re-enabled once disabled for a warehouse and is not the targeted fix for poor query plans.
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