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Microsoft DP-600 Exam Syllabus Topics:

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

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q141-Q146):

NEW QUESTION # 141
Hotspot Question
You have a Microsoft Power BI semantic model.
You plan to implement calculation groups.
You need to create a calculation item that will change the context from the selected date to month-to-date (MTD).
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
https://learn.microsoft.com/en-us/dax/selectedmeasure-function-dax#example


NEW QUESTION # 142
You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse!
contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?

Answer: D


NEW QUESTION # 143
You are building a Microsoft Fabric solution that will use a Microsoft Power Bl semantic model named Model 1 as a data source Users will ask plain-English Questions about sales measures and dimensions in Modell, You need to ensure that the solution generates answers grounded in Modell. The solution must be reusable outside a single Power Bl report What should you use?

Answer: B


NEW QUESTION # 144
Hotspot Question
You have two Microsoft Power BI queries named Employee and Retired Roles.
You need to merge the Employee query with the Retired Roles query. The solution must ensure that duplicate rows in each query are removed.
Which column and Join Kind should you use in Power Query Editor? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point.

Answer:

Explanation:


NEW QUESTION # 145
You have a Fabric tenant that contains a workspace named Workspace1.
You plan to deploy a semantic model named Model1 by using the XMLA endpoint.
You need to optimize the deployment of Model1. The solution must minimize how long it takes to deploy Model1.
What should you do in Workspace1?

Answer: D

Explanation:
When using XMLA endpoint for deployment or processing , the semantic model storage format impacts deployment times.
Small storage format allows faster metadata operations and shorter deployment times.
Large semantic model storage format is only needed for extremely large models ( > 10 GB dataset limit), and it increases deployment overhead.
The other options (B: service editing, C: cache for shortcuts) are unrelated to XMLA endpoint deployment performance.
Reference:
Power BI semantic model storage format
answer: A. Select Small semantic model storage format.


NEW QUESTION # 146
......

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