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Microsoft DP-700 Exam Syllabus Topics:

TopicDetails
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 (Q20-Q25):

NEW QUESTION # 20
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?

Answer: B

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 # 21
You have a Fabric tenant that is linked to a Microsoft Entra tenant. The Microsoft [intra tenant contains a user named User1 and two groups named Group! and Groups.
The Fabric tenant contains the workspaces shown in the following table.

You need to meet the following access requirements:
* User1 must have read access to the following:
* The operations department reports
* The HR department warehouse and reports
* The legal department warehouse and reports
* User1 must be able to create new items in Workspace
* Group1 must have access to the finance department warehouse and reports.
* Group1 must be able to add new users to the finance department workspace.
* Group2 must have access to only the HR warehouse and the legal warehouse.
The solution must follow the principle of least privilege.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 22
You have a Google Cloud Storage (GCS) container named storage1 that contains the files shown in the following table.

You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the shortcuts shown in the following table.

You need to read data from all the shortcuts.
Which shortcuts will retrieve data from the cache?

Answer: D

Explanation:
When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).
Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to
12 hours, it is likely that this data will be retrieved from the cache, as it hasn't been too long since it was last accessed.
Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period.
Therefore, this data will also be retrieved from the cache.
Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it's outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.


NEW QUESTION # 23
You have a Fabric workspace named Workspace1 that uses version control. Workspace! and Azure DevOps are integrated.
In Azure DevOps. developers create a branch named Branch1 to test extract, transform, and load (ETL) updates.
You need to connect Workspace1 to Branch1. The solution must ensure that all the existing content in Branch1 is available in Workspace1.
What should you do?

Answer: A


NEW QUESTION # 24
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.

Answer:

Explanation:


NEW QUESTION # 25
......

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