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Microsoft DP-700 Exam Syllabus Topics:

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

Section

Weight

Objectives

Ingest and transform data

30–35%

- Design and implement data loading patterns

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

- Transform data

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

- 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

Implement and manage an analytics solution

30–35%

- Configure workspace settings

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

- Manage data lifecycle and governance

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

- Manage data storage

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

Monitor and optimize an analytics solution

30–35%

- Monitor analytics solutions

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

- Troubleshoot issues

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

- Optimize performance

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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q61-Q66):

NEW QUESTION # 61
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on-premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?

A. a KQL queryset
B. a data pipeline
C. a notebook
D. a Dataflow Gen1 dataflow

Answer: B

Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Microsoft Fabric, the best option is to use a data pipeline. A data pipeline in Fabric allows for the orchestration of data movement, from source to destination, using connectors, transformations, and scheduled workflows. Since the data is being transferred from an on-premises database and requires the use of a data gateway, a data pipeline provides the appropriate framework to facilitate this data movement efficiently and reliably.

NEW QUESTION # 62
HOTSPOT
You have a Fabric workspace.
You are debugging a statement and discover the following issues:
Sometimes, the statement fails to return all the expected rows.
The PurchaseDate output column is NOT in the expected format of mmm dd, yy.
You need to resolve the issues. The solution must ensure that the data types of the results are retained. The results can contain blank cells.
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 # 63
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.
You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.
You need to minimize how long it takes to query Table1.
What should you do?

A. Run the OPTIMIZE and VACUUM commands.
B. Disable V-Order and run the OPTIMIZE command.
C. Disable V-Order and run the VACUUM command.

Answer: A

Explanation:
Problem Overview:
Solution:
Commands and Their Roles:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
- Removes old, unreferenced data files and metadata from the Delta table.
- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

NEW QUESTION # 64
HOTSPOT
You have a Fabric workspace.
You are debugging a statement and discover the following issues:
Sometimes, the statement fails to return all the expected rows.
The PurchaseDate output column is NOT in the expected format of mmm dd, yy.
You need to resolve the issues. The solution must ensure that the data types of the results are retained. The results can contain blank cells.
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 # 65
You need to develop an orchestration solution in fabric that will load each item one after the other. The solution must be scheduled to run every 15 minutes. Which type of item should you use?

A. data pipeline
B. Dataflow Gen2 dataflow
C. notebook
D. warehouse

Answer: A

NEW QUESTION # 66
......

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