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Microsoft DP-700 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q79-Q84):

NEW QUESTION # 79
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. No
B. Yes

Answer: A

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 # 80
You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.
(Click the Exhibit tab.) What will occur the next time Pipelinel tuns?

A. Execute procedure1 will run first, and then Copy_kdi will run.
B. Copy.kdi will run first, and then Execute procedurel will run.
C. Execute procedurel will run and Copy_kdi will be skipped.
D. Copy.kdi will run and Execute procedurel will be skipped.
E. Both activities will be skipped.
F. Both activities will run simultaneously.

Answer: E

NEW QUESTION # 81
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. Data pipeline
B. Streaming dataset
C. Event stream
D. Dataflow Gen2

Answer: A

NEW QUESTION # 82
You need to return records ingested during the last 540 seconds.
Which KQL filter should you use?

A. | where SecondsAgo = datetime_diff( ' second ' , now(), ingestion_time()) < = 540
B. | where SecondsAgo = datetime_diff( ' second ' , now(), JobStartTime) < = 540
C. | where SecondsAgo = datetime_diff( ' second ' , ingestion_time(), now()) < = 540
D. | where SecondsAgo = datetime_diff( ' second ' , JobEndTime, JobStartTime) < = 540

Answer: A

NEW QUESTION # 83
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. queryinsights.long_running_queries
C. queryinsights.frequently_run_queries
D. sys.dm_exec_requests
E. sys.dm_exec_connections

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 # 84
......

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