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Microsoft DP-600 Exam Syllabus Topics:

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

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q179-Q184):

NEW QUESTION # 179
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:
To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:
* From Performance analyzer, capture a recording.
* View the Server Timings tab.
* Enable Query Timings and Server Timings. Run the query.
* View the Query Timings tab.
* Sort the Duration (ms) column in descending order by DAX query time.


NEW QUESTION # 180
Drag and Drop Question
You have a Fabric workspace that contains a Dataflow Gen2 query. The query returns the following data.

You need to filter the results to ensure that only the latest version of each customer's record is retained. The solution must ensure that no new columns are loaded to the semantic model.
Which four actions should you perform in sequence in Power Query Editor? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 181
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
REFRESH TABLE customer
Does this meet the goal?

Answer: B


NEW QUESTION # 182
You need to recommend a solution to group the Research division workspaces.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Comprehensive Detailed Explanation
The requirement is to logically group the Research division workspaces and support filtering in the OneLake data hub.
Why Domain?
Microsoft Fabric allows you to assign workspace domains (e.g., Finance, HR, Research).
This provides a logical grouping of workspaces within the organization.
Domains are the only supported way to enable filtering in OneLake data hub by department.
Capacity or tenant grouping would not allow filtering by department in OneLake.
Why OneLake data hub?
The OneLake data hub is the centralized interface to browse and discover data assets across the organization.
With workspace domains configured, users can filter directly in the OneLake data hub by domain (e.g.,
"Research").
The Fabric Admin portal and Entra admin center are used for administration, not for workspace filtering in data discovery.
Correct Selection for the Hotspot
Grouping method: Domain
Tool: OneLake data hub
References
Workspace domains in Microsoft Fabric
Discover and filter data in OneLake data hub
Final Answer:
Grouping method = Domain
Tool = OneLake data hub


NEW QUESTION # 183
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

Answer: C

Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.


NEW QUESTION # 184
......

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