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Certification Topics of Microsoft DP-500 Exam

  • Implement and manage data models (25 -30%)

  • Implement and manage a data analytics environment (25-30%)

  • Explore and visualize data (20-25%)

  • Query and transform data (20-25%)

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-500

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Microsoft DP-500 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Query and transform data20–25%- Integrate data sources
  • 1. Implement incremental refresh
  • 2. Combine on-premises and cloud data
  • 3. Configure data privacy and security
- Transform data with Power Query
  • 1. Handle complex data sources
  • 2. Manage parameters and functions
  • 3. Optimize query logic and performance
  • 4. Design scalable dataflows
- Ingest and process data in Azure Synapse
  • 1. Use Apache Spark for data transformation
  • 2. Load data into dedicated SQL pools
  • 3. Query data using serverless SQL pool
  • 4. Optimize query performance
Topic 2: Implement and manage a data analytics environment25–30%- Manage Azure Synapse Analytics workspace and resources
  • 1. Provision and configure Synapse workspace
  • 2. Monitor and optimize Synapse performance
  • 3. Manage SQL pools and Spark pools
  • 4. Configure security and access control
- Governance and lifecycle management
  • 1. Apply data governance with Microsoft Purview
  • 2. Implement version control and CI/CD
  • 3. Monitor usage and audit activity
  • 4. Configure tenant and workspace settings
- Manage Power BI Premium and scalability
  • 1. Configure large dataset storage
  • 2. Implement deployment pipelines
  • 3. Manage workspaces and datasets
  • 4. Provision and assign Premium capacity
Topic 3: Explore and visualize data20–25%- Deploy and distribute analytics solutions
  • 1. Monitor report usage and health
  • 2. Publish and share content securely
  • 3. Embed reports in applications and portals
  • 4. Manage app workspaces
- Enable advanced analytics
  • 1. Use AI visuals and insights
  • 2. Implement custom visuals using Python or R
  • 3. Integrate Azure Machine Learning models
- Build enterprise-scale reports
  • 1. Design for performance and accessibility
  • 2. Configure real-time refresh
  • 3. Implement custom visuals and interactivity
  • 4. Create paginated reports
Topic 4: Implement and manage data models25–30%- Design tabular data models
  • 1. Implement row-level security
  • 2. Choose import, DirectQuery, or composite mode
  • 3. Define calculated tables and columns
  • 4. Create relationships and hierarchies
- Use DAX for calculations
  • 1. Create measures and calculation groups
  • 2. Optimize DAX query performance
  • 3. Use advanced DAX patterns
  • 4. Implement time intelligence
- Optimize model performance
  • 1. Manage aggregations
  • 2. Tune DirectQuery behavior
  • 3. Reduce cardinality and data size
  • 4. Configure query reduction settings

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