Microsoft Fabric Analytics Engineer Associate DP-600

Microsoft Fabric Analytics Engineer Associate (DP-600)

The Microsoft Fabric Analytics Engineer Associate (DP-600) course at Linux Training Academy is designed for data professionals, analysts, and IT learners who want to build expertise in modern data analytics using Microsoft Fabric.

This course focuses on designing, implementing, and managing analytics solutions using Microsoft Fabric, enabling learners to work with data integration, transformation, storage, and visualization in a unified platform.

Course Overview

This program provides in-depth training on data engineering, data analytics, and business intelligence workflows using Microsoft Fabric. Learners will gain hands-on experience in building end-to-end analytics solutions, from data ingestion to reporting.

What You Will Learn

  • Introduction to Microsoft Fabric
  • Data Engineering Concepts
  • Data Integration and Transformation
  • Working with Data Warehouses
  • Real-Time Analytics
  • Data Modeling and Optimization
  • Power BI Integration and Reporting

Course Duration

Duration: 45 to 60 Days

Why Choose This Course?

  • Industry-recognized certification (DP-600)
  • High-demand data analytics skills
  • Hands-on training with real-world scenarios
  • Covers end-to-end analytics workflow
  • Guidance from experienced trainers

Career Opportunities

After completing this course, you can explore roles such as:

  • Data Analyst
  • Analytics Engineer
  • Business Intelligence Developer
  • Data Engineer (Entry Level)
  • Power BI Developer

Who Can Join?

  • Students interested in data analytics
  • IT professionals and developers
  • Data analysts and aspiring engineers
  • Anyone with basic knowledge of databases and Excel

Microsoft Fabric Analytics Engineer Associate [DP-600]

Modules

1. Maintain a Data Analytics Solution (25-30%)

  • 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, data 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 Data (45-50%)

  • Create a data connection
  • Discover data by using OneLake data hub and real-time hub
  • Ingest or access data as needed
  • Choose between a lakehouse, warehouse, or eventhouse
  • Implement OneLake integration for eventhouse and semantic models
  • Create views, functions, and stored procedures
  • Enrich data by adding new columns or tables
  • Implement a star schema for a lakehouse or warehouse
  • Denormalize, aggregate, merge, or join data
  • Identify and resolve duplicate data, missing data, or null values
  • Convert column data types
  • Filter 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

    • 3. Implement and Manage Semantic Models (25–30%)

  • Choose a storage model
  • 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
  • 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
  • Implement performance improvements in queries and report visuals
  • Improve DAX performance
  • Configure Direct Lake, including default fallback and refresh behavior
  • Implement incremental refresh for semantic models

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