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Microsoft DP-700 Exam Syllabus Topics:
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NEW QUESTION # 19
You have a table in a Fabric lakehouse that contains the following data.
You have a notebook that contains 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:
Explanation:
NEW QUESTION # 20
HOTSPOT
You need to troubleshoot the ad-hoc query issue.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
A screenshot of a computer Description automatically generated
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.
NEW QUESTION # 21
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.
NEW QUESTION # 22
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 23
You have a Fabric workspace named Workspacel that contains the following items:
* A Microsoft Power Bl report named Reportl
* A Power Bl dashboard named Dashboardl
* A semantic model named Modell
* A lakehouse name Lakehouse1
Your company requires that specific governance processes be implemented for the items. Which items can you endorse in Fabric?
- A. Lakehouse1, Model1, and Report1 only
- B. Report1 and Dashboard1 only
- C. Model1, Report1, and Dashboard1 only
- D. Lakehouse1, Modell, Report1 and Dashboard1
- E. Lakehouse1, Modell, and Dashboard1 only
Answer: D
NEW QUESTION # 24
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:
Explanation:
Topic 2, Contoso, Ltd
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company's IT department has a team of data analysts and a team of data engineers that use analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company's website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
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:
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:
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:
Items that relate to data ingestion must meet the following requirements:
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:
NEW QUESTION # 25
HOTSPOT
You need to troubleshoot the ad-hoc query issue.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
A screenshot of a computer Description automatically generated
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.
NEW QUESTION # 26
HOTSPOT
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:
Explanation:
Explanation:
NEW QUESTION # 27
You have a KQL database that contains a table named Readings.
You need to build a KQL query to compare the Meter-Reading value of each row to the previous row base on the ilmestamp value A sample of the expected output is shown in the following table.
Answer:
Explanation:
Explanation:
NEW QUESTION # 28
You need to recommend a Fabric streaming solution that will use the sources shown in the following table.
The solution must minimize development effort.
What should you include in the recommendation for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 29
HOTSPOT
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.
You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 30
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:
NEW QUESTION # 31
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_sessions
- B. sys.dm_exec_connections
- C. queryinsights.frequently_run_queries
- D. sys.dm_exec_requests
- E. queryinsights.long_running_queries
Answer: A
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.
NEW QUESTION # 32
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.
NEW QUESTION # 33
You need to recommend a solution to resolve the MAR1 connectivity issues. The solution must minimize development effort. What should you recommend?
- A. Configure Fault tolerance for the Copy data activity.
- B. Add a ForEach activity to the data pipeline.
- C. Configure retries for the Copy data activity.
- D. Call a notebook from the data pipeline.
Answer: C
NEW QUESTION # 34
You need to resolve the sales data issue. The solution must minimize the amount of data transferred.
What should you do?
- A. Configure incremental refresh for the dataflow. Set Store rows from the past to 1 Month.
- B. Spilt the dataflow into two dataflows.
- C. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Year.
- D. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Month.
- E. Configure scheduled refresh for the dataflow.
Answer: D
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.
Topic 2, Contoso, LtdCase 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.
NEW QUESTION # 35
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:
Explanation:
NEW QUESTION # 36
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse! contains a table named Customer. Customer contains the following data.
You have an internal Microsoft Entra user named User1 that has an email address of [email protected].
You need to provide User1 with access to the Customer table. The solution must prevent User1 from accessing the CreditCard column.
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:
Explanation:
NEW QUESTION # 37
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:
Explanation:
NEW QUESTION # 38
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