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Microsoft DP-700 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitor and optimize an analytics solution	30–35%	- Troubleshoot issues 1. Identify and resolve pipeline errors 2. Identify and resolve storage and query errors 3. Identify and resolve notebook and Spark errors 4. Identify and resolve Dataflow errors - Monitor analytics solutions 1. Monitor semantic model refreshes 2. Configure alerts and notifications 3. Monitor Fabric items and activities 4. Monitor data ingestion and transformation - Optimize performance 1. Optimize pipelines and data flows 2. Optimize storage efficiency 3. Optimize Spark and query performance 4. Optimize Lakehouse and data warehouse performance
Topic 2: Ingest and transform data	30–35%	- Design and implement data loading patterns 1. Design streaming data loading patterns 2. Prepare data for dimensional models 3. Design full and incremental data loads - Transform data 1. Transform data using SQL 2. Transform data using KQL 3. Transform data using PySpark 4. Clean, denormalize, and aggregate data - Orchestrate data operations 1. Orchestrate with notebooks and pipelines 2. Use parameters and dynamic expressions 3. Implement schedules and triggers - Ingest data 1. Ingest data using Eventstreams 2. Ingest data using pipelines 3. Ingest data using Dataflows Gen2
Topic 3: Implement and manage an analytics solution	30–35%	- Manage data lifecycle and governance 1. Manage data access and security 2. Implement data retention and archiving 3. Implement data governance with Microsoft Purview - Configure workspace settings 1. Configure Spark workspace settings 2. Configure OneLake workspace settings 3. Configure domain workspace settings 4. Configure Dataflows Gen2 settings 5. Configure Microsoft Fabric workspace settings - Manage data storage 1. Implement data mirroring 2. Create and manage OneLake shortcuts 3. Choose appropriate data stores

Section

Weight

Objectives

Topic 1: Monitor and optimize an analytics solution

30–35%

- Troubleshoot issues

1. Identify and resolve pipeline errors
2. Identify and resolve storage and query errors
3. Identify and resolve notebook and Spark errors
4. Identify and resolve Dataflow errors

- Monitor analytics solutions

1. Monitor semantic model refreshes
2. Configure alerts and notifications
3. Monitor Fabric items and activities
4. Monitor data ingestion and transformation

- Optimize performance

1. Optimize pipelines and data flows
2. Optimize storage efficiency
3. Optimize Spark and query performance
4. Optimize Lakehouse and data warehouse performance

Topic 2: Ingest and transform data

30–35%

- Design and implement data loading patterns

1. Design streaming data loading patterns
2. Prepare data for dimensional models
3. Design full and incremental data loads

- Transform data

1. Transform data using SQL
2. Transform data using KQL
3. Transform data using PySpark
4. Clean, denormalize, and aggregate data

- Orchestrate data operations

1. Orchestrate with notebooks and pipelines
2. Use parameters and dynamic expressions
3. Implement schedules and triggers

- Ingest data

1. Ingest data using Eventstreams
2. Ingest data using pipelines
3. Ingest data using Dataflows Gen2

Topic 3: Implement and manage an analytics solution

30–35%

- Manage data lifecycle and governance

1. Manage data access and security
2. Implement data retention and archiving
3. Implement data governance with Microsoft Purview

- Configure workspace settings

1. Configure Spark workspace settings
2. Configure OneLake workspace settings
3. Configure domain workspace settings
4. Configure Dataflows Gen2 settings
5. Configure Microsoft Fabric workspace settings

- Manage data storage

1. Implement data mirroring
2. Create and manage OneLake shortcuts
3. Choose appropriate data stores

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Prüfungsfragen mit Lösungen (Q111-Q116):

111. Frage
You have a Fabric workspace that contains a semantic model named Modell. You need to monitor the refresh history of Model 1 and visualize the refresh history in a chart. What should you use?

A. a data pipeline
B. the refresh history from the settings of Model1.
C. a Dataflow Gen2 dataflow
D. a notebook

Antwort: D

112. Frage
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Table 1. You need to ensure that a user named User 1 can view only specific rows in Table1. What should you do first?

A. Create a security policy.
B. Create a function.
C. Grant User1 the SELECT permission for Table1.
D. Create a security predicate.

Antwort: D

113. Frage
You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports.
A user named User1 wants to use SQL to analyze the data in Lakehouse1.
You need to configure access for User1. The solution must meet the following requirements:
Provide User1 with read access to the table data in Lakehouse1.
Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1.
Prevent User1 from accessing other items in Workspace1.
What should you do?

A. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
B. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
C. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.
D. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.

Antwort: B

Begründung:
To meet the specified requirements for User1, the solution must ensure:
Read access to the table data in Lakehouse1: User1 needs permission to access the data within Lakehouse1. By sharing Lakehouse1 with User1 and selecting the Read all SQL endpoint data option, User1 will be able to query the data via SQL endpoints.
Prevent Apache Spark usage: By sharing the lakehouse directly and selecting the SQL endpoint data option, you specifically enable SQL-based access to the data, preventing User1 from using Apache Spark to query the data.
Prevent access to other items in Workspace1: Assigning User1 the Viewer role for Workspace1 ensures that User1 can only view the shared items (in this case, Lakehouse1), without accessing other resources such as notebooks, pipelines, or Power BI reports within Workspace1.
This approach provides the appropriate level of access while restricting User1 to only the required resources and preventing access to other workspace assets.

114. Frage
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. Yes
B. No

Antwort: B

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

115. Frage
You have three users named User1, User2, and User3.
You have the Fabric workspaces shown in the following table.

You have a security group named Group1 that contains User1 and User3.
The Fabric admin creates the domains shown in the following table.

User1 creates a new workspace named Workspace3.
You add Group1 to the default domain of Domain1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

116. Frage
......

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