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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain a data solution10-15%- Deploy data assets
  • 1. Implement CI/CD for Fabric items
  • 2. Use deployment pipelines for development to production
  • 3. Automate deployments using APIs and scripts
- Maintain a data solution
  • 1. Manage workspace and capacity settings
  • 2. Optimize query performance
  • 3. Implement data refresh strategies
Topic 2: Secure and monitor data solutions15-20%- Secure data solutions
  • 1. Use Microsoft Purview for data governance
  • 2. Configure sensitive data classifications
  • 3. Implement column-level and row-level security
  • 4. Configure workspace and item permissions
- Monitor data solutions
  • 1. Implement alerting and notifications
  • 2. Monitor pipeline and dataflow execution
  • 3. Use OneLake monitoring capabilities
  • 4. Review and analyze capacity metrics
Topic 3: Load and prepare data20-25%- Create and configure items
  • 1. Configure data processing with notebooks
  • 2. Create and configurehortcuts
  • 3. Create and configure Lakehouse, Warehouse, or data pipeline
- Ingest data from source systems
  • 1. Implement streaming data ingestion with Eventstream
  • 2. Use Data Factory copy activity for batch ingestion
  • 3. Configure Data Gateway for hybrid scenarios
  • 4. Use Data Factory data flow for transformation
  • 5. Ingest data using PySpark or Spark SQL
Topic 4: Clean, transform, and enrich data25-30%- Clean data
  • 1. Validate data quality using Data Quality Profiling
  • 2. Handle missing values and duplicates
  • 3. Apply data cleansing techniques
- Transform data
  • 1. Use Dataflow Gen2 for transformations
  • 2. Perform schema evolution and mapping
  • 3. Use Spark libraries for data transformation
  • 4. Implement data standardization and normalization
- Enrich data
  • 1. Merge and join data sources
  • 2. Implement slowly changing dimensions (SCD)
  • 3. Implement incremental data loading
Topic 5: Design and manage the data model20-25%- Implement and configure a data model
  • 1. Implement row-level security (RLS)
  • 2. Use DirectLake mode for large datasets
  • 3. Configure SQL analytics endpoint
  • 4. Create and manage semantic models
- Design a data model
  • 1. Implement dimension and fact tables
  • 2. Design a star or snowflake schema
  • 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 (Q174-Q179):

NEW QUESTION # 174
Hotspot Question
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_row contains the following table:

You create a Fabric notebook and attach it to Lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
- Add a column named pickupDate that will contain only the date portion of pickupDateTime.
- Filter the DataFrame to include only rows where fareAmount is a
positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 175
What is the key feature of creating an empty materialized view?

Answer: B

Explanation:
Option C is CORRECT because Creating an empty materialized view results in it being instantly available for query after creation, as there are no existing records to process.
Reference:
https://learn.microsoft.com/en-us/fabric/real-time-intelligence/materialized-view


NEW QUESTION # 176
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2 and a user named User1.
You need to ensure that User1 can perform the following tasks:
* Create a new domain.
* Create two subdomains named subdomain1 and subdomain2.
* Assign Workspace1 to subdomain1.
* Assign Workspace2 to subdomain2.
The solution must follow the principle of least privilege.
Which role should you assign to User1?

Answer: A


NEW QUESTION # 177
You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dawset named DS1. You need to ensure read-write access to DS1 is available by using the XMLA endpoint. What should be modified first?

Answer: C

Explanation:
To ensure read-write access to DS1 is available by using the XMLA endpoint, the C1 settings (which refer to the capacity settings) should be modified first. XMLA endpoint configuration is a capacity feature, not specific to individual datasets or workspaces. References = The configuration of XMLA endpoints in Power BI capacities is detailed in the Power BI documentation on dataset management.


NEW QUESTION # 178
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 179
......

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