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Microsoft DP-700 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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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DP-700 Valid Exam Syllabus | DP-700 Valid Test Answers

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q47-Q52):

NEW QUESTION # 47
You have an Azure SQL database named DBl
In a Fabric workspace, you deploy an eventstream named EventSlreamDBI to stream record changes from DB1 into a lakehouse.
You discover that events are NOT being propagated to EventStreamDBL You need to ensure that the events are propagated to EventStreamDBl. What should you do?

A. Create an Azure Stream Analytics job.
B. Enable Extended Events for DBl
C. Enable change data capture (CDC) for DB1.
D. Create a read-only replica of DB1.

Answer: C

NEW QUESTION # 48
You have a Fabric workspace that contains a large table named Table1. Table1 contains 2 billion rows.
You have a data source that generates a data file every 30 minutes The file contains only changes that occurred since the last file was generated.
You plan to deploy a data pipeline that will process each data file when it is generated and load the contents into Table1. You need to recommend which loading pattern to use for the following operations:
* Create new records.
* Delete existing records.
The solution must support the versioning of existing records.
What should you recommend for each operation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 49
You have a Fabric workspace named Workspace1 that contains three notebooks named notebook_01, notebook_02, and notebook_03.
You are building a new notebook in Workspace1 that will contain the following Directed Acyclic Graph (DAG) definition.

You need to modify the DAG definition to meet the following requirements:
* Ensure that notebook.01 and notebook_02 run in parallel.
* Ensure that notebook_02 only runs after the execution of notebook_03 is complete.
How should you modify the DAG definition?

A. Move the notebook_03 declaration to the top of the DAG definition. Add a concurrency to notebook_02 and notebook_01.
B. Move the declaration of notebook 03 to the top of the DAG definition. Add a new stage that contains notebook 01 and notebook 02.
C. Create two stages in the DAG definition. Add notebook_03 to the first stage. Add notebook_01 and notebook_02 to the second stage.
D. Create a second DAG definition. Move notebook_03 and notebook_02 to the new DAG definition

Answer: C

NEW QUESTION # 50
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. Yes
B. No

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 # 51
You have a KQL database that contains a table named Readings.
You need to build a KQL query to compare the Meter-Reading value of each row to the previous row base on the ilmestamp value A sample of the expected output is shown in the following table.

Answer:

Explanation:

Explanation:

NEW QUESTION # 52
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