Exam DP-600T00-A: Microsoft Fabric Analytics Practice Test

Course Overview

• Course Title: Exam DP-600T00-A: Microsoft Fabric Analytics Practice Test
• Instructor: Sanjay Parmar
• Target Audience: BI developers, data engineers, analytics professionals
• Prerequisites: None

Curriculum Highlights

• Key Topics Covered:
  • Introduction to Microsoft Fabric
    • Fabric architecture and components overview
    • Dataflows, Pipelines, Notebooks: Introduction and use cases
    • Semantic models, data warehouses, and lakehouses: Basics
  • Data Preparation and Integration
    • Creating and managing dataflows
    • Pipelines for orchestration
    • Data cleansing, transformation, and enrichment
  • Semantic Models and Analytics Assets
    • Building and managing semantic models
    • Developing data warehouses and lakehouses
    • End-to-end analytics pipelines using Fabric
  • Programming for Fabric Analytics
    • Writing queries with SQL
    • Data exploration with KQL (Kusto Query Language)
    • Implementing calculations with DAX (Data Analysis Expressions)
    • Notebook creation and advanced analytics
  • Data Modeling and Visualization
    • Best practices for data modeling
    • Connecting to visualization tools like Power BI
    • Optimizing models for performance and usability
  • Optimization and Performance Tuning
    • Monitoring pipeline performance
    • Optimizing queries and transformations
    • Best practices for scalability and speed in large-scale solutions
  • Security and Governance
    • Implementing data security and compliance
    • Managing roles, permissions, and data integrity
    • Auditing practices in Fabric
  • Real-World Scenarios and Use Cases
    • Designing end-to-end pipelines
    • Building and deploying warehouses, lakehouses, and semantic models
    • Troubleshooting Fabric solutions
  • Comprehensive Final Exam
    • DP-600 exam structure and strategies
    • Mixed practice questions from all topics for final preparation
• Key Skills Learned:
  • Data preparation and integration techniques
  • Building and managing semantic models
  • Writing queries with SQL, KQL, and DAX
  • Data modeling and visualization best practices
  • Optimization and performance tuning
  • Implementing data security and governance
  • Real-world scenario application

Course Format

• Duration: 5 practice tests
• Format: Self-paced online course
• Resources: Access on mobile, step-by-step explanations for each question