Exam Microsoft DP-700 Tests - Valid DP-700 Test Sample

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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. Implement data governance with Microsoft Purview 3. Manage data access and security - Configure workspace settings 1. Configure Spark workspace settings 2. Configure domain workspace settings 3. Configure OneLake workspace settings 4. Configure Microsoft Fabric workspace settings 5. Configure Dataflows Gen2 settings - Manage data storage 1. Choose appropriate data stores 2. Implement data mirroring 3. Create and manage OneLake shortcuts
Topic 2: Monitor and optimize an analytics solution	30–35%	- Monitor analytics solutions 1. Monitor semantic model refreshes 2. Monitor Fabric items and activities 3. Configure alerts and notifications 4. Monitor data ingestion and transformation - 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 pipeline errors 2. Identify and resolve storage and query errors 3. Identify and resolve Dataflow errors 4. Identify and resolve notebook and Spark errors
Topic 3: Ingest and transform data	30–35%	- Transform data 1. Transform data using PySpark 2. Transform data using SQL 3. Transform data using KQL 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. Use parameters and dynamic expressions 2. Orchestrate with notebooks and pipelines 3. Implement schedules and triggers - Ingest data 1. Ingest data using Dataflows Gen2 2. Ingest data using Eventstreams 3. Ingest data using pipelines

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. Implement data governance with Microsoft Purview
3. Manage data access and security

- Configure workspace settings

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

- Manage data storage

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

Topic 2: Monitor and optimize an analytics solution

30–35%

- Monitor analytics solutions

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

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

Topic 3: Ingest and transform data

30–35%

- Transform data

1. Transform data using PySpark
2. Transform data using SQL
3. Transform data using KQL
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. Use parameters and dynamic expressions
2. Orchestrate with notebooks and pipelines
3. Implement schedules and triggers

- Ingest data

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

>> Exam Microsoft DP-700 Tests <<

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q53-Q58):

NEW QUESTION # 53
You have a Fabric warehouse named DW1 that contains four staging tables named ProductCategory, ProductSubcategory, Product, and SalesOrder. ProductCategory, ProductSubcategory, and Product are used often in analytical queries.
You need to implement a star schema for DW1. The solution must minimize development effort.
Which design approach should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 54
You have a Fabric workspace named Workspace1 that is connected to a GitHub repository named repo1.
Workspace1 contains the items shown in the following table.

You modify Semantic model 1, Semanticmodel2, and Report2.
You need to commit the changes to repo1.
What is the minimum number of commits you should perform?

A. 0
B. A
C. 1
D. 2

Answer: A

NEW QUESTION # 55
You have a Fabric workspace named Workspace1 that contains the items shown in the following table.

For Model1, the Keep your Direct Lake data up to date option is disabled.
You need to configure the execution of the items to meet the following requirements:
Notebook1 must execute every weekday at 8:00 AM.
Notebook2 must execute when a file is saved to an Azure Blob Storage container.
Model1 must refresh when Notebook1 has executed successfully.
How should you orchestrate each item? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 56
You have a Fabric workspace named WS1 that is connected to the main branch of an Azure DevOps Git repository. WS1 contains a lakehouse a notebook and a pipeline.
Two data engineer must update the notebook and people concurrently for separate features.
You need to configure version control to support isolated feature development and controlled integration into the main branch.
The solution must meet the following requirements:
Prevent one engineer's reviewed workspace changes from affecting the other engineer workspace.
Support a review before changes are integrated into the main branch.
Track changes to the Fabric items in Git.
How should you configure the development workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 57
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 Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns:
BikepointID
Street
Neighbourhood
No_Bikes
No_Empty_Docks
Timestamp
You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
Solution: You use the following code segment:

Does this meet the goal?

A. Yes
B. no

Answer: B

Explanation:
This code does not meet the goal because this is an SQL-like query and cannot be executed in KQL, which is required for the database.
Correct code should look like:

NEW QUESTION # 58
......

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