LATEST STUDY DP-700 QUESTIONS OFFER YOU ACCURATE NEW EXAM DURATION TO PASS IMPLEMENTING DATA ENGINEERING SOLUTIONS USING MICROSOFT FABRIC EXAM

Latest Study DP-700 Questions offer you accurate New Exam Duration to pass Implementing Data Engineering Solutions Using Microsoft Fabric exam

Latest Study DP-700 Questions offer you accurate New Exam Duration to pass Implementing Data Engineering Solutions Using Microsoft Fabric exam

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q60-Q65):

NEW 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:
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: A

Explanation:
Filter Condition: It correctly filters rows where Neighbourhood is "Sands End" and No_Bikes is greater than or equal to 15.
Sorting: The sorting is explicitly done by No_Bikes in ascending order using sort by No_Bikes asc.
Projection: It projects the required columns (BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp), which minimizes the data returned for consumption.


NEW QUESTION # 61
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. Only Folder1 is created and Pipeline1 moves to Folder1.
  • B. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1.
  • C. Only Pipeline1 and Lakehouse1 are deployed.
  • D. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed.

Answer: D

Explanation:
When you restructure Workspace1 by adding a new folder (Folder1) and moving Pipeline1 into it, deployPipeline1 will deploy the entire structure of Workspace1 to Workspace2, preserving the changes made in Workspace1. This includes:
Folder1 will be created in Workspace2, mirroring the structure in Workspace1.
Pipeline1 will be moved into Folder1 in Workspace2, maintaining the same folder structure.
Lakehouse1 will be deployed to Workspace2 as it exists in Workspace1.


NEW QUESTION # 62
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 dynamic allocation for the Spark pool.
  • B. Change the runtime version.
  • C. Enable high concurrency for notebooks.
  • D. Increase the number of executors.

Answer: C

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.


NEW QUESTION # 63
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:

Explanation:


NEW QUESTION # 64
You need to schedule the population of the medallion layers to meet the technical requirements.
What should you do?

  • A. Schedule multiple data pipelines.
  • B. Schedule a data pipeline that calls other data pipelines.
  • C. Schedule a notebook.
  • D. Schedule an Apache Spark job.

Answer: B

Explanation:
The technical requirements specify that:
Why Use a Data Pipeline That Calls Other Data Pipelines?
- Sequential execution of child pipelines.
- Error handling to send email notifications upon failures.
- Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).


NEW QUESTION # 65
......

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