DP-600 Valid Test Sample - DP-600 Prep Guide

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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Clean, transform, and enrich data25-30%- Clean data
  • 1. Apply data cleansing techniques
  • 2. Validate data quality using Data Quality Profiling
  • 3. Handle missing values and duplicates
- Transform data
  • 1. Implement data standardization and normalization
  • 2. Perform schema evolution and mapping
  • 3. Use Spark libraries for data transformation
  • 4. Use Dataflow Gen2 for transformations
- Enrich data
  • 1. Merge and join data sources
  • 2. Implement slowly changing dimensions (SCD)
  • 3. Implement incremental data loading
Secure and monitor data solutions15-20%- Monitor data solutions
  • 1. Use OneLake monitoring capabilities
  • 2. Implement alerting and notifications
  • 3. Review and analyze capacity metrics
  • 4. Monitor pipeline and dataflow execution
- Secure data solutions
  • 1. Configure sensitive data classifications
  • 2. Configure workspace and item permissions
  • 3. Implement column-level and row-level security
  • 4. Use Microsoft Purview for data governance
Deploy and maintain a data solution10-15%- Deploy data assets
  • 1. Use deployment pipelines for development to production
  • 2. Implement CI/CD for Fabric items
  • 3. Automate deployments using APIs and scripts
- Maintain a data solution
  • 1. Implement data refresh strategies
  • 2. Optimize query performance
  • 3. Manage workspace and capacity settings
Load and prepare data20-25%- Ingest data from source systems
  • 1. Configure Data Gateway for hybrid scenarios
  • 2. Use Data Factory data flow for transformation
  • 3. Implement streaming data ingestion with Eventstream
  • 4. Use Data Factory copy activity for batch ingestion
  • 5. Ingest data using PySpark or Spark SQL
- Create and configure items
  • 1. Create and configurehortcuts
  • 2. Configure data processing with notebooks
  • 3. Create and configure Lakehouse, Warehouse, or data pipeline
Design and manage the data model20-25%- Implement and configure a data model
  • 1. Configure SQL analytics endpoint
  • 2. Use DirectLake mode for large datasets
  • 3. Create and manage semantic models
  • 4. Implement row-level security (RLS)
- Design a data model
  • 1. Design a star or snowflake schema
  • 2. Implement dimension and fact tables
  • 3. Choose appropriate data model type (lakehouse vs warehouse)
  • 4. Define relationships and hierarchies

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q103-Q108):

NEW QUESTION # 103
Which type of data store should you recommend in the AnalyticsPOC workspace?

Answer: D


NEW QUESTION # 104
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1 and a warehouse named Warehouse1.
You need to create a new table in Warehouse1 named POSCustomers by querying the customer table in Lakehouse1.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 105
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?

Answer: B


NEW QUESTION # 106
You have a Microsoft Power B1 report and a semantic model that uses Direct Lake mode. From Power Si Desktop, you open Performance analyzer as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
The Direct Lake fallback behavior is set to: DirectQueryOnly
The query for the table visual is executed by using: DirectQuery
In the context of Microsoft Power BI, when using DirectQuery in Direct Lake mode, there is no caching of data and all queries are sent directly to the underlying data source. The Performance Analyzer tool shows the time taken for different operations, and from the options provided, it indicates that DirectQuery mode is being used for the visuals, which is consistent with the Direct Lake setting. DirectQueryOnly as the fallback behavior ensures that only DirectQuery will be used without reverting to import mode.


NEW QUESTION # 107
You have a Microsoft Entra tenant named contoso.com and an external user named userl@fabrikam.com.
You have a Fabric workspace named Workspace1 that contains a semantic model named Model! and a report named Report 1. User1@fabrikam.com has access to Report1.
You enable read-write on the XML for Analysis (XMLA) endpoint to provide advanced semantic modeling by using DAX Studio.
You need to provide user! @fabrikam.com with a URL that will enable connectivity by using DAX Studio.
How should you complete the URL? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 108
......

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