PRACTICE TEST DP-700 FEE, DP-700 VALID TEST FORMAT

Practice Test DP-700 Fee, DP-700 Valid Test Format

Practice Test DP-700 Fee, DP-700 Valid Test Format

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q14-Q19):

NEW QUESTION # 14
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. a semantic link in a notebook
  • B. Monitoring hub
  • C. dynamic management views in Microsoft SQL Server Management Studio
  • D. dynamic management views in Azure Data Studio

Answer: A

Explanation:
Semantic models in Microsoft Fabric are part of Power BI datasets and require refreshes to stay updated with the latest data.
Dynamically executing and monitoring the refresh progress requires a tool or approach that integrates with Fabric's capabilities for semantic models.


NEW QUESTION # 15
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:
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:
This code does not meet the goal because it uses sort by without specifying the order, which defaults to ascending, but explicitly mentioning asc improves clarity.
Correct code should look like:


NEW QUESTION # 16
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 # 17
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 incremental refresh for the dataflow. Set Store rows from the past to 1 Month.
  • C. Configure scheduled refresh for the dataflow.
  • D. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Month.
  • E. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Year.

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.


NEW QUESTION # 18
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:
What should you do?

  • A. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.
  • B. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
  • C. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
  • D. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.

Answer: C

Explanation:
To meet the specified requirements for User1, the solution must ensure:
Read access to the table data in Lakehouse1: User1 needs permission to access the data within Lakehouse1. By sharing Lakehouse1 with User1 and selecting the Read all SQL endpoint data option, User1 will be able to query the data via SQL endpoints.
Prevent Apache Spark usage: By sharing the lakehouse directly and selecting the SQL endpoint data option, you specifically enable SQL-based access to the data, preventing User1 from using Apache Spark to query the data.
Prevent access to other items in Workspace1: Assigning User1 the Viewer role for Workspace1 ensures that User1 can only view the shared items (in this case, Lakehouse1), without accessing other resources such as notebooks, pipelines, or Power BI reports within Workspace1.
This approach provides the appropriate level of access while restricting User1 to only the required resources and preventing access to other workspace assets.


NEW QUESTION # 19
......

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