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HybridCloudWorks
AssociateDP-600

Implementing Analytics Solutions Using Microsoft Fabric

Validate expertise implementing analytics solutions using Microsoft Fabric including lakehouses, data pipelines, and semantic models.

Study Hours
40h
Prep Time
~3 months
Avg. Pass Rate
73%

Topics Covered

Microsoft Fabric
Lakehouse
Data Pipelines
Semantic Models
Power BI

Skills measured

Official study guide

Microsoft's own objectives, in the order the exam presents them. Each area links to its section on Microsoft Learn. Checked 2026-10-26.

  1. Maintain a data analytics solution25–30%
    This area on Microsoft Learn

    Implement security and governance

    • Implement workspace-level access controls
    • Implement item-level access controls
    • Implement row-level, column-level, object-level, and file-level access control
    • Apply sensitivity labels to items
    • Endorse items

    Maintain the analytics development lifecycle

    • Configure version control for a workspace
    • Create and manage a Power BI Desktop project (.pbip)
    • Create and configure deployment pipelines
    • Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models
    • Deploy and manage semantic models by using the XMLA endpoint
    • Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
  2. Prepare data45–50%
    This area on Microsoft Learn

    Get data

    • Create a data connection
    • Discover data by using OneLake catalog and Real-Time hub
    • Ingest or access data as needed
    • Choose between different data stores
    • Implement OneLake integration for Eventhouse and semantic models

    Transform data

    • Create views, functions, and stored procedures
    • Enrich data by adding new columns or tables
    • Implement a star schema for a lakehouse or warehouse
    • Denormalize data
    • Aggregate data
    • Merge or join data
    • Identify and resolve duplicate data, missing data, or null values
    • Convert column data types
    • Filter data

    Query and analyze data

    • Select, filter, and aggregate data by using the Visual query editor
    • Select, filter, and aggregate data by using SQL
    • Select, filter, and aggregate data by using KQL
    • Select, filter, and aggregate data by using DAX
  3. Implement and manage semantic models25–30%
    This area on Microsoft Learn

    Design and build semantic models

    • Choose a storage mode
    • Implement a star schema for a semantic model
    • Implement relationships, such as bridge tables and many-to-many relationships
    • Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
    • Implement calculation groups, dynamic format strings, and field parameters
    • Identify use cases for and configure large semantic model storage format
    • Design and build composite models

    Optimize enterprise-scale semantic models

    • Implement performance improvements in queries and report visuals
    • Improve DAX performance
    • Configure Direct Lake, including default fallback and refresh behavior
    • Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint
    • Implement incremental refresh for semantic models