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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	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	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.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q81-Q86):

NEW QUESTION # 81
You need to recommend a solution to resolve the MAR1 connectivity issues. The solution must minimize development effort. What should you recommend?

A. Call a notebook from the data pipeline.
B. Configure Fault tolerance for the Copy data activity.
C. Configure retries for the Copy data activity.
D. Add a ForEach activity to the data pipeline.

Answer: C

NEW QUESTION # 82
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.
You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.
You need to minimize how long it takes to query Table1.
What should you do?

A. Run the OPTIMIZE and VACUUM commands.
B. Disable V-Order and run the OPTIMIZE command.
C. Disable V-Order and run the VACUUM command.

Answer: A

Explanation:
Problem Overview:
Solution:
Commands and Their Roles:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
- Removes old, unreferenced data files and metadata from the Delta table.
- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

NEW QUESTION # 83
Your company has three newly created data engineering teams named Team1, Team2, and Team3 that plan to use Fabric. The teams have the following personas:
* Team1 consists of members who currently use Microsoft Power BI. The team wants to transform data by using by a low-code approach.
* Team2 consists of members that have a background in Python programming. The team wants to use PySpark code to transform data.
* Team3 consists of members who currently use Azure Data Factory. The team wants to move data between source and sink environments by using the least amount of effort.
You need to recommend tools for the teams based on their current personas.
What should you recommend for each team? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 84
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. Enable high concurrency for notebooks.
C. Increase the number of executors.
D. Change the runtime version.

Answer: B

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 # 85
You have a Fabric workspace that contains a lakehouse named Lakehousel.
You plan to create a data pipeline named Pipeline! to ingest data into Lakehousel. You will use a parameter named paraml to pass an external value into Pipeline1!. The paraml parameter has a data type of int You need to ensure that the pipeline expression returns param1 as an int value.
How should you specify the parameter value?

A. "@{pipeline().parameters.[paraml]}"
B. "@{pipeline().parameters.paraml}-
C. "@pipeline(). parameters. paraml"
D. "@{pipeline().parameters.paraml}"

Answer: D

NEW QUESTION # 86
......

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Regular Updates in Real Microsoft DP-700 Exam Questions

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Microsoft DP-700 Exam Syllabus Topics:

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