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Microsoft DP-700 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Ingest and transform data

30–35%

- Transform data

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

- Design and implement data loading patterns

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

- Orchestrate data operations

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

- Ingest data

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

Topic 2: Implement and manage an analytics solution

30–35%

- Manage data lifecycle and governance

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

- Configure workspace settings

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

- Manage data storage

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

Topic 3: 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

- Optimize performance

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

- Monitor analytics solutions

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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q40-Q45):

NEW QUESTION # 40
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. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.
B. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
C. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
D. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.

Answer: C

Explanation:
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.

NEW QUESTION # 41
HOTSPOT
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains the following tables and columns.

You need to denormalize the tables and include the ContractType and StartDate columns in the Employee table. The solution must meet the following requirements:
Ensure that the StartDate column is of the date data type.
Ensure that all the rows from the Employee table are preserved and include any matching rows from the Contract table.
Ensure that the result set displays the total number of employees per contract type for all the contract types that have more than two employees.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 42
You have a Fabric .
In Warehouse1, you create a table named DimCustomer by running the following statement.

You need to set the Customerkey column as a primary key of the DimCustomer table.
Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

NEW QUESTION # 43
You have a Fabric data warehouse that contains the following tables.

You need to refresh the tables by using an automated pipeline. The solution must ensure that table updates occur in the correct order to maintain referential integrity.
Which two tables should you refresh first? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. FactSale
B. Dim Product
C. DimGeography
D. DimCustomer
E. Factlnventory

Answer: B,C

NEW QUESTION # 44
You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded daily into Warehouse1 by using data pipelines and stored procedures.
You discover that the daily data load takes longer than expected.
You need to monitor Warehouse1 to identify the names of users that are actively running queries.
Which view should you use?

A. sys.dm_exec_sessions
B. sys.dm_exec_requests
C. sys.dm_exec_connections
D. queryinsights.long_running_queries
E. queryinsights.frequently_run_queries

Answer: A

Explanation:
sys.dm_exec_sessions provides real-time information about all active sessions, including the user, session ID, and status of the session. You can filter on session status to see users actively running queries.

NEW QUESTION # 45
......

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