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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	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.
Topic 3	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.

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q118-Q123):

NEW QUESTION # 118
You have a Fabric notebook named Notebook1 that has been executing successfully for the last week.
During the last run, Notebook1executed nine jobs.
You need to view the jobs in a timeline chart.
What should you use?

A. the run series from the details of the application run
B. Spark History Server
C. the job history from the application run
D. Real-Time hub
E. Monitoring hub

Answer: D

Explanation:
The run series from the details of the application run is the most detailed and relevant feature for visualizing job execution in a timeline format, making it the correct choice for this scenario. It provides an intuitive way to analyze job execution patterns and improve the efficiency of the notebook.

NEW QUESTION # 119
You are implementing the following data entities in a Fabric environment:
Entity1: Available in a lakehouse and contains data that will be used as a core organization entity Entity2: Available in a semantic model and contains data that meets organizational standards Entity3: Available in a Microsoft Power BI report and contains data that is ready for sharing and reuse Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making Your company requires that specific governance processes be implemented for the data.
You need to apply endorsement badges to the entities based on each entity's use case.
Which badge should you apply to each entity? To answer, drag the appropriate badges the correct entities. Each badge may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 120
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 add the make_list() function to the output columns.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.

NEW QUESTION # 121
You need to ensure that the authors can see only their respective sales data.
How should you complete the statement? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 122
You have a Fabric workspace that contains 10 data pipelines named Pipeline1 through Pipeline10. The data pipelines load, transform, and publish data to a lakehouse named Lakehouse1.
Each data pipeline takes 30 minutes to run. The data pipelines must run in numerical sequence based on their names. The end-to-end process must start every eight hours.
You need to configure orchestration to ensure that all the data pipelines run in the correct order. The solution must minimize the number of schedules and administrative effort.
What should you do?

A. Add an event-based trigger to each data pipeline so that the data pipeline starts when a new dataset is written to Lakehouse1.
B. Configure a time-based schedule to run each data pipeline independently every eight hours.
C. Schedule Pipeline1 to run every eight hours and configure trigger dependencies so that each subsequent data pipeline starts when the previous data pipeline completes successfully.
D. Create a master data pipeline that runs every eight hours and uses an Invoke pipeline activity to call the other data pipelines sequentially.

Answer: D

NEW QUESTION # 123
......

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