DP-700 Exam Dumps - Secret To Pass In First Attempt [2026]

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Microsoft DP-700 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - 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 pipelines 2. Ingest data using Dataflows Gen2 3. Ingest data using Eventstreams - Transform data 1. Clean, denormalize, and aggregate data 2. Transform data using PySpark 3. Transform data using SQL 4. Transform data using KQL
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 - Manage data storage 1. Implement data mirroring 2. Create and manage OneLake shortcuts 3. Choose appropriate data stores - Configure workspace settings 1. Configure OneLake workspace settings 2. Configure domain workspace settings 3. Configure Microsoft Fabric workspace settings 4. Configure Dataflows Gen2 settings 5. Configure Spark workspace settings
Topic 3: Monitor and optimize an analytics solution	30–35%	- Troubleshoot issues 1. Identify and resolve notebook and Spark errors 2. Identify and resolve pipeline errors 3. Identify and resolve Dataflow errors 4. Identify and resolve storage and query errors - 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 storage efficiency 2. Optimize Spark and query performance 3. Optimize pipelines and data flows 4. Optimize Lakehouse and data warehouse performance

Section

Weight

Objectives

Topic 1: 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

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

- Transform data

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

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

- Manage data storage

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

- Configure workspace settings

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

Topic 3: Monitor and optimize an analytics solution

30–35%

- Troubleshoot issues

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

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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q130-Q135):

NEW QUESTION # 130
You have a Fabric workspace that contains a warehouse named Warehouse!. Warehousel contains a table named DimCustomers. DimCustomers contains the following columns:
* CustomerName
* CustomerlD
* BirthDate
* Email
You need to configure security to meet the following requirements:
* BirthDate in DimCustomer must be masked and display 1900-01-01.
* Email in DimCustomer must be masked and display only the first leading character and the last five characters.
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 # 131
HOTSPOT
You have a Fabric workspace named Workspace1_DEV that contains the following items:
10 reports
Four notebooks
Three lakehouses
Two data pipelines
Two Dataflow Gen1 dataflows
Three Dataflow Gen2 dataflows
Five semantic models that each has a scheduled refresh policy
You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST.
You deploy all the items from Workspace1_DEV to Workspace1_TEST.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 132
HOTSPOT
You need to troubleshoot the ad-hoc query issue.
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:
A screenshot of a computer Description automatically generated

SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.
FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries using the queryinsights.long_running_queries view, which provides insights into queries that take longer than expected to execute.
WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.
AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause failures and need attention.

NEW QUESTION # 133
HOTSPOT
You are building a data loading pattern for Fabric notebook workloads.
You have the following code segment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 134
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. an activator
C. a data pipeline trigger
D. a job definition

Answer: A

NEW QUESTION # 135
......

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