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Microsoft DP-700 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitor and optimize an analytics solution	30–35%	- Optimize performance 1. Optimize Spark and query performance 2. Optimize Lakehouse and data warehouse performance 3. Optimize storage efficiency 4. Optimize pipelines and data flows - Troubleshoot issues 1. Identify and resolve pipeline errors 2. Identify and resolve notebook and Spark errors 3. Identify and resolve storage and query 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
Topic 2: Implement and manage an analytics solution	30–35%	- Manage data storage 1. Create and manage OneLake shortcuts 2. Implement data mirroring 3. Choose appropriate data stores - Manage data lifecycle and governance 1. Manage data access and security 2. Implement data governance with Microsoft Purview 3. Implement data retention and archiving - Configure workspace settings 1. Configure Dataflows Gen2 settings 2. Configure Microsoft Fabric workspace settings 3. Configure domain workspace settings 4. Configure OneLake workspace settings 5. Configure Spark workspace settings
Topic 3: Ingest and transform data	30–35%	- Orchestrate data operations 1. Implement schedules and triggers 2. Use parameters and dynamic expressions 3. Orchestrate with notebooks and pipelines - Ingest data 1. Ingest data using pipelines 2. Ingest data using Dataflows Gen2 3. Ingest data using Eventstreams - Transform data 1. Transform data using KQL 2. Clean, denormalize, and aggregate data 3. Transform data using PySpark 4. Transform data using SQL - 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

Section

Weight

Objectives

Topic 1: Monitor and optimize an analytics solution

30–35%

- Optimize performance

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

- Troubleshoot issues

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

Topic 2: Implement and manage an analytics solution

30–35%

- Manage data storage

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

- Manage data lifecycle and governance

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

- Configure workspace settings

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

Topic 3: Ingest and transform data

30–35%

- Orchestrate data operations

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

- Ingest data

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

- Transform data

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

- 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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Prüfungsfragen mit Lösungen (Q132-Q137):

132. Frage
DRAG DROP
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData.
The following is a sample of the data in TaxiData.

You need to build two KQL queries. The solution must meet the following requirements:
One of the queries must partition RunningTotalAmount by VendorID.
The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

Partition the RunningTotalAmount by VendorID. - Row_cumsum
The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).

Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type - Row_window_session

133. Frage
You have a Fabric notebook named Notebook1 that has been executing successfully for the last week.
During the last run, Notebook1executed nine jobs.
You need to view the jobs in a timeline chart.
What should you use?

A. Monitoring hub
B. the run series from the details of the application run
C. Spark History Server
D. Real-Time hub
E. the job history from the application run

Antwort: D

Begründung:
The run series from the details of the application run is the most detailed and relevant feature for visualizing job execution in a timeline format, making it the correct choice for this scenario. It provides an intuitive way to analyze job execution patterns and improve the efficiency of the notebook.

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

Antwort: A

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.

135. Frage
You have an Azure SQL database named DB1. The data in DB1 is appended daily.
You have a Fabric workspace named Workspace1 that contains a semanlk model named Model!. Modell is sourced from DB1. You need to configure Model1 to refresh only new and changed data during scheduled refreshes What should you do?

A. In Model1, manually partition large tables by using monthly partitions.
B. For Workspace1, select Large semantic model storage format
C. For Model1, change storage mode to Import
D. From Power Query, define the RangeStart and RangeEnd parameters and filter table rows by using the parameters.

Antwort: D

136. Frage
You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.
(Click the Exhibit tab.) What will occur the next time Pipelinel tuns?

A. Both activities will run simultaneously.
B. Execute procedurel will run and Copy_kdi will be skipped.
C. Copy.kdi will run and Execute procedurel will be skipped.
D. Copy.kdi will run first, and then Execute procedurel will run.
E. Execute procedure1 will run first, and then Copy_kdi will run.
F. Both activities will be skipped.

Antwort: F

137. Frage
......

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