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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.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-700/

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions:

Question #1

You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub.
You need to identify in which workspace a specific item runs.
Which column should you view in Monitoring hub?

A. Capacity
B. Job type
C. Start time
D. Location
E. Activity name
F. Item type
G. Submitter

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).

Question #2

You have a Fabric workspace that contains a takehouse and a semantic model named Model1.
You use a notebook named Notebook1 to ingest and transform data from an external data source.
You need to execute Notebook1 as part of a data pipeline named Pipeline1. The process must meet the following requirements:
* Run daily at 07:00 AM UTC.
* Attempt to retry Notebook1 twice if the notebook fails.
* After Notebook1 executes successfully, refresh Model1.
Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. Set the Retry setting of the Semantic model refresh activity to 2.
B. Place the Semantic model refresh activity after the Notebook activity and link the activities by using an On completion condition.
C. From the Schedule settings of Notebook1, set the time zone to UTC.
D. From the Schedule settings of Pipeline1, set the time zone to UTC.
E. Place the Semantic model refresh activity after the Notebook activity and link the activities by using the On success condition.
F. Set the Retry setting of the Notebook activity to 2.

Reveal Solution Discussion 0

Correct Answer: D,E,F 🗳️

Question #3

HOTSPOT
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.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Question #4

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?