Microsoft DP-700 Exam Syllabus & Latest DP-700 Exam Notes

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Microsoft DP-700 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Implement and manage an analytics solution

30–35%

- Manage data lifecycle and governance

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

- Manage data storage

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

- Configure workspace settings

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

Topic 2: Ingest and transform data

30–35%

- Orchestrate data operations

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

- Ingest data

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

- Transform data

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

- Design and implement data loading patterns

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

Topic 3: Monitor and optimize an analytics solution

30–35%

- Optimize performance

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

- Troubleshoot issues

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

- Monitor analytics solutions

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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q54-Q59):

NEW QUESTION # 54
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. Execute procedurel will run and Copy_kdi will be skipped.
B. Copy.kdi will run first, and then Execute procedurel will run.
C. Copy.kdi will run and Execute procedurel will be skipped.
D. Both activities will run simultaneously.
E. Both activities will be skipped.
F. Execute procedure1 will run first, and then Copy_kdi will run.

Answer: E

NEW QUESTION # 55
You have a Fabric workspace that contains a semantic model named Model1.
You need to dynamically execute and monitor the refresh progress of Model1.
What should you use?

A. dynamic management views in Azure Data Studio
B. a semantic link in a notebook
C. Monitoring hub
D. dynamic management views in Microsoft SQL Server Management Studio

Answer: C

Explanation:
Semantic models in Microsoft Fabric are part of Power BI datasets and require refreshes to stay updated with the latest data.
Dynamically executing and monitoring the refresh progress requires a tool or approach that integrates with Fabric's capabilities for semantic models.

NEW QUESTION # 56
You have a Fabric warehouse named Warehouse1 that contains a table named Table1. Table1 contains the following columns that store string values:
* FirstName
* Phone
* Email
You have two Microsoft Entra security groups named Group1 and Group2. The members of both groups can query Table1 and see full values for FirstName, Phone, and Email.
You need to configure Table1 to meet the following requirements:

Answer:

Explanation:

Explanation:

NEW QUESTION # 57
You need to create the product dimension.
How should you complete the Apache Spark SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
A screenshot of a computer Description automatically generated

Join between Products and ProductSubCategories:
Use an INNER JOIN.
The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that only matching records (i.e., products with a valid subcategory) are included.
Join between ProductSubCategories and ProductCategories:
Use an INNER JOIN.
Similar to the above logic, we want to include only subcategories assigned to a valid product category. An INNER JOIN ensures this condition is met.
WHERE Clause
Condition: IsActive = 1
Only active products (where IsActive equals 1) should be included in the gold layer. This filters out inactive products.

NEW QUESTION # 58
You have an Azure subscription that contains a blob storage account named sa1. Sa1 contains two files named Filelxsv and File2.csv.
You have a Fabric tenant that contains the items shown in the following table.

You need to configure Pipeline1 to perform the following actions:
* At 2 PM each day, process Filel.csv and load the file into flhl.
* At 5 PM each day. process File2.csv and load the file into flhl.
The solution must minimize development effort. What should you use?

A. a data pipeline schedule
B. a job definition
C. an activator
D. a data pipeline trigger

Answer: A

NEW QUESTION # 59
......

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