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Microsoft DP-700 Exam Syllabus Topics:

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

Section

Weight

Objectives

Ingest and transform data

30–35%

- Ingest data

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

- 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. Implement schedules and triggers
3. Orchestrate with notebooks and pipelines

- Transform data

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

Implement and manage an analytics solution

30–35%

- Manage data lifecycle and governance

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

- Manage data storage

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

- Configure workspace settings

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

Monitor and optimize an analytics solution

30–35%

- Troubleshoot issues

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

- 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

- Optimize performance

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

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q34-Q39):

NEW QUESTION # 34
You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1.
You have an Azure SQL database named Source1 that has public internet access disabled.
You need to ensure that Job1 can access the data in Source1.
What should you create?

A. an on-premises data gateway
B. a managed private endpoint
C. a data management gateway
D. an integration runtime

Answer: B

NEW QUESTION # 35
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. Disable V-Order and run the VACUUM command.
B. Disable V-Order and run the OPTIMIZE command.
C. Run the OPTIMIZE and VACUUM commands.

Answer: C

Explanation:
Problem Overview:
Table1 has 2,000 small Parquet files (1 MB each).
Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant overhead.
Solution:
To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.
Commands and Their Roles:
OPTIMIZE Command:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
VACUUM Command:
- 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 # 36
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to Workspace1. You create a table by running the following statement.

You need to ensure that the team can view only the first two characters and the last four characters of the Creditcard attribute.
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 # 37
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 KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.
You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.
Solution: You change project to extend.
Does this meet the goal?

A. Yes
B. No

Answer: B

Explanation:
Using extend retains all columns in the table, potentially increasing the size of the output unnecessarily.
project is more efficient because it selects only the required columns.

NEW QUESTION # 38
You have a Fabric warehouse named DW1 that contains a Type 2 slowly changing dimension (SCD) dimension table named DimCustomer. DimCustomer contains 100 columns and 20 million rows. The columns are of various data types, including int, varchar, date, and varbinary.
You need to identify incoming changes to the table and update the records when there is a change. The solution must minimize resource consumption.
What should you use to identify changes to attributes?

A. a direct attributes comparison for the attributes in the source table.
B. a direct attributes comparison across the attributes in the DimCustomer table.
C. a hash function to compare the attributes in the source table.
D. a hash function to compare the attributes in the DimCustomer table.

Answer: B

NEW QUESTION # 39
......

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