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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain a data solution10-15%- Maintain a data solution
  • 1. Manage workspace and capacity settings
  • 2. Optimize query performance
  • 3. Implement data refresh strategies
- 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
Topic 2: Secure and monitor data solutions15-20%- Secure data solutions
  • 1. Use Microsoft Purview for data governance
  • 2. Configure workspace and item permissions
  • 3. Configure sensitive data classifications
  • 4. Implement column-level and row-level security
- Monitor data solutions
  • 1. Monitor pipeline and dataflow execution
  • 2. Use OneLake monitoring capabilities
  • 3. Review and analyze capacity metrics
  • 4. Implement alerting and notifications
Topic 3: Load and prepare data20-25%- Create and configure items
  • 1. Create and configure Lakehouse, Warehouse, or data pipeline
  • 2. Configure data processing with notebooks
  • 3. Create and configurehortcuts
- Ingest data from source systems
  • 1. Ingest data using PySpark or Spark SQL
  • 2. Configure Data Gateway for hybrid scenarios
  • 3. Use Data Factory data flow for transformation
  • 4. Implement streaming data ingestion with Eventstream
  • 5. Use Data Factory copy activity for batch ingestion
Topic 4: Design and manage the data model20-25%- 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
- Implement and configure a data model
  • 1. Use DirectLake mode for large datasets
  • 2. Configure SQL analytics endpoint
  • 3. Implement row-level security (RLS)
  • 4. Create and manage semantic models
Topic 5: 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
- Enrich data
  • 1. Implement incremental data loading
  • 2. Merge and join data sources
  • 3. Implement slowly changing dimensions (SCD)
- 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

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric DP-600 Prüfungsfragen mit Lösungen (Q77-Q82):

77. Frage
You have a Fabric tenant that contains a lakehouse named LH1.
You need to deploy a new semantic model. The solution must meet the following requirements:
* Support complex calculated columns that include aggregate functions, calculated tables, and Multidimensional Expressions (MDX) user hierarchies.
* Minimize page rendering times.
How should you configure the model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

Supports complex calculated columns (with aggregate functions, calculated tables, MDX hierarchies).
Minimizes page rendering times.
Step 1 - Choosing the Mode
Direct Lake # Best for near real-time queries, avoids duplication, but has limitations (e.g., some complex calculated columns, MDX user hierarchies are not fully supported).
DirectQuery # Sends queries to the source each time. It supports complex expressions but is slow (not optimal for minimizing page rendering times).
Import # Data is loaded into VertiPaq in-memory engine, supports full DAX capabilities, calculated tables, MDX hierarchies, and provides fastest query performance.
# Correct choice: Import.
Step 2 - Choosing Query Caching
Capacity default # Relies on the workspace/capacity setting.
Off # Disables caching, which could slow down report rendering.
On # Ensures queries are cached for faster page rendering times.
# Correct choice: On.
Final Answer:
Mode: Import
Query Caching: On
References:
Semantic model storage modes in Fabric
Query caching in Power BI / Fabric


78. Frage
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named I.H1 and a warehouse named DW1. I.H1 contains a table named signindata that is in the dho schema.
You need to create a stored procedure in DW1 that deduplicates the data in the signindata table.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Fach correct selection is worth one point.

Antwort:

Begründung:

Explanation:


79. Frage
You have Fabric tenant that contains four workspaces named Development, Test, QA, and Production. All the workspaces are in Premium Per User (PPU) license mode.
You plan to use a release pipeline to support the development lifecycle from Development to Production.
Which three 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.

Antwort:

Begründung:

Explanation:

Scenario Recap
Fabric tenant with workspaces: Development, Test, QA, Production.
All workspaces are already in Premium Per User (PPU) mode (which is required for deployment pipelines).
Task: Set up a release pipeline to move through Dev # Test # QA # Production.
Step 1: Create a deployment pipeline
The first step is always to create a new deployment pipeline in the Fabric/Power BI service. This establishes the structure for Dev # Test # Prod (and optionally QA).
Step 2: Create the QA stage
By default, pipelines have 3 stages (Dev, Test, Prod).
Since we need a QA stage in addition, we must explicitly add it as a stage to the pipeline.
Step 3: Assign workspaces
Once the pipeline structure is ready (with QA included), we assign each workspace to the corresponding stage:
Development # Development
Test # Test
QA # QA
Production # Production
Why Not the Other Actions?
Move each workspace to Pro license mode # Incorrect, because deployment pipelines require Premium capacity or PPU, and the workspaces already are in PPU mode.
Create a deployment rule # Deployment rules (e.g., parameter or data source rules) are optional for environment-specific changes, but not part of the initial required setup sequence.
References
Deployment pipelines overview
Assigning workspaces to deployment pipelines


80. Frage
Drag and Drop Question
You have Fabric tenant that contains a semantic model named Model1. Model1 contains the following tables.

You are designing a Microsoft Power BI report named Report1 that will deliver near-real-time (NRT) shipping data. Report1 will contain data about many shipped packages.
You need to ensure that each table is in the appropriate storage mode to support Report1. The solution must support near-real-time data delivery while maintaining high query performance.
Which storage mode should you select for each table? To answer, drag the appropriate storage modes to the correct tables. Each mode 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.

Antwort:

Begründung:


81. Frage
You have a Fabric workspace named Workspace-!. You have a GitHub repository named Repol. You need to connect Workspacel to the main branch of Repol. Which information should you provide?

Antwort: C


82. Frage
......

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