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Microsoft DP-600 Exam Syllabus Topics:

Section	Weight	Objectives
Maintain a data analytics solution	25–30	- Implement security and governance 1. Use sensitivity labels and endorsement 2. Configure workspace and item-level access 3. Apply row-level, column-level, and object-level security - Manage analytics development lifecycle 1. Configure version control and projects 2. Perform impact analysis and dependency management 3. Implement deployment pipelines
Implement and manage semantic models	25–30	- Design and build semantic models 1. Define relationships, hierarchies, and measures 2. Optimize model performance and structure 3. Create Power BI semantic models - Deploy and maintain semantic models 1. Use XMLA endpoint for deployment and management 2. Create reusable assets and shared models 3. Monitor and refresh semantic models
Prepare data for analytics	45–50	- Clean and transform data 1. Process data using Spark notebooks and SQL 2. Perform data enrichment and validation 3. Manage data quality and consistency - Implement data storage structures 1. Design and manage lakehouse tables 2. Implement delta lake and partitioning 3. Configure warehouse storage and querying - Ingest and load data 1. Ingest data from various sources 2. Use Dataflows Gen2 to transform data 3. Load data into lakehouses and warehouses

Section

Weight

Objectives

Maintain a data analytics solution

25–30

- Implement security and governance

1. Use sensitivity labels and endorsement
2. Configure workspace and item-level access
3. Apply row-level, column-level, and object-level security

- Manage analytics development lifecycle

1. Configure version control and projects
2. Perform impact analysis and dependency management
3. Implement deployment pipelines

Implement and manage semantic models

25–30

- Design and build semantic models

1. Define relationships, hierarchies, and measures
2. Optimize model performance and structure
3. Create Power BI semantic models

- Deploy and maintain semantic models

1. Use XMLA endpoint for deployment and management
2. Create reusable assets and shared models
3. Monitor and refresh semantic models

Prepare data for analytics

45–50

- Clean and transform data

1. Process data using Spark notebooks and SQL
2. Perform data enrichment and validation
3. Manage data quality and consistency

- Implement data storage structures

1. Design and manage lakehouse tables
2. Implement delta lake and partitioning
3. Configure warehouse storage and querying

- Ingest and load data

1. Ingest data from various sources
2. Use Dataflows Gen2 to transform data
3. Load data into lakehouses and warehouses

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These DP-600 exam question formats contain real, valid, and updated Microsoft DP-600 exam questions that will assist you in Microsoft Implementing Analytics Solutions Using Microsoft Fabric exam preparation and enable you to pass the challenging Microsoft DP-600 Exam with good scores. The Microsoft DP-600 questions are prepared by highly experienced professionals and, thus, are kept to the point and concise.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q156-Q161):

NEW QUESTION # 156
You have a Fabric tenant that contains a semantic model named model1. The two largest columns in model1 are shown in the following table.

You need to optimize model 1. The solution must meet the following requirements:
* Reduce the model size.
* Increase refresh performance when using Import mode.
* Ensure that the datetime value for each sales transaction is available in the model.
What should you do on each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 157
You have a data warehouse that contains the following tables:

You have a T-SQL query named Query1 that contains the following statements.

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 # 158
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Tablet.
You are creating a new data pipeline.
You plan to copy external data to Table1. The schema of the external data changes regularly.
You need the copy operation to meet the following requirements:
- Replace Table1 with the schema of the external data.
- Replace all the data in Table1 with the rows in the external data.
You add a Copy data activity to the pipeline.
What should you do for the Copy data activity?

A. From the Source tab, select Recursively.
B. From the Source tab, select Enable partition discovery.
C. From the Settings tab, select Enable staging.
D. From the Destination tab, set Table action to Overwrite.
E. From the Source tab, add additional columns.

Answer: D

Explanation:
Enable "Truncate table" option: This option truncates the target table before copying data, ensuring that all existing data is replaced with the new data from the external source.

NEW QUESTION # 159
You have a Fabric tenant that contains a semantic model named Model1.
Model1 contains the following tables.

Answer:

Explanation:

Explanation:

NEW QUESTION # 160
You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

A. Unpivot columns
B. Split column
C. Remove other columns
D. Unpivot other columns
E. Group by

Answer: D

Explanation:
The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.

NEW QUESTION # 161
......

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