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Free PDF 2025 Reliable Microsoft DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Valid Test Pass4sure

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Microsoft DP-700 Exam Syllabus Topics:

Topic
Details

Topic 1

Ingest and transform data: This section of the exam measures the skills of Data Engineers that cover designing and implementing data loading patterns. It emphasizes preparing data for loading into dimensional models, handling batch and streaming data ingestion, and transforming data using various methods. A skill to be measured is applying appropriate transformation techniques to ensure data quality.

Topic 2

Implement and manage an analytics solution: This section of the exam measures the skills of Microsoft Data Analysts regarding configuring various workspace settings in Microsoft Fabric. It focuses on setting up Microsoft Fabric workspaces, including Spark and domain workspace configurations, as well as implementing lifecycle management and version control. One skill to be measured is creating deployment pipelines for analytics solutions.

Topic 3

Monitor and optimize an analytics solution: This section of the exam measures the skills of Data Analysts in monitoring various components of analytics solutions in Microsoft Fabric. It focuses on tracking data ingestion, transformation processes, and semantic model refreshes while configuring alerts for error resolution. One skill to be measured is identifying performance bottlenecks in analytics workflows.

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Microsoft DP-700 Pdf Format Practice Program

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q95-Q100):

NEW QUESTION # 95
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. queryinsights.frequently_run_queries
B. sys.dm_exec_requests
C. sys.dm_exec_sessions
D. sys.dm_exec_connections
E. queryinsights.long_running_queries

Answer: C

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 # 96
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. Monitoring hub
B. dynamic management views in Azure Data Studio
C. a semantic link in a notebook
D. dynamic management views in Microsoft SQL Server Management Studio

Answer: C

NEW QUESTION # 97
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 # 98
HOTSPOT
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:

Explanation:

NEW QUESTION # 99
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 # 100
......

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