Microsoft Certified Fabric Analytics Engineer (DP-600) Exam Preparation

Jun 25, 2024

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Microsoft Certified Fabric Analytics Engineer (DP-600) Exam Preparation - Course Overview

Introduction

  • Microsoft Certified Fabric Analytics Engineer (DP-600) exam certification course.
  • Aim: Provide necessary knowledge to pass the DP-600 exam.
  • Exam overview: Certification details, logistics, and study approach.

Exam Content & Course Structure

Section 1: Planning, Implementing, and Managing Solutions for Data Analytics

  1. Understanding Requirements: Identify requirements for solutions, security concerns, data gateways, access controls, workspace setup, capacity management, etc.
  2. Version Control: Use Azure DevOps for version control, deployment pipeline settings, configuration details.
  3. Key Topics: Version control setup, Azure DevOps configuration, deployment pipeline, security and settings.

Section 2: Preparation and Serving of Data (40-45% of Exam)

  1. Core Tasks: Creating tables, lakehouses and warehouses, T-SQL experience, shortcuts, data ingestion methods.
  2. Data Transformation: T-SQL, Spark, and DAX basics required for the exam. Optimize performance for Spark jobs, T-SQL scripts, and data transformation techniques.
  3. Performance Optimization: Strategies to monitor performance, optimize data ingestion and transformation, handling large datasets with efficiency.

Section 3: Implementing and Managing Semantic Models (20-25%)

  1. Storage Modes: DirectQuery, Import Mode, DirectLake Mode – understanding when to use each mode.
  2. Data Modeling: Star schemas, bridge tables, handling many-to-many relationships, security models in Power BI for RLS/OLS.
  3. Integration with External Tools: Usage of DAX Studio, Tabular Editor for optimized semantic models.

Section 4: Explore and Analyze Data (20-25%)

  1. Data Analysis Skills: Analyze data using T-SQL, profiling data tables, using Power BI visual queries, integrating XMLA endpoints.
  2. Advanced Analytics: Understanding descriptive, diagnostic, and predictive analytics within Power BI.

Section 5: Exam Logistics and Study Approach

  1. Exam Structure: 40-60 questions, multiple-choice, drag-and-drop, case studies, etc.
  2. Scoring: Scaled between 0-1,000, with a passing score of 700.
  3. Exam Considerations: Online vs. in-person exam settings, setting up, preparing, accessing sandbox.
  4. Free Exam Offer: Conditions for free exam attempts using given resources such as AI skills challenge training course.

Course Style and Learning Community Support

  1. Content Delivery: Real-world scenarios, combining theory with practice, reinforcing knowledge with questions and full practice papers.
  2. Resources: Content posted in the school community, use of notebooks, scripts, and further resources for an in-depth study.
  3. Community Engagement: Join the community for free, gain access to all resources, additional learning links.
  4. Learning Pathway: Provides structured learning, aiding new jobs, promotions, especially in consulting roles where certifications help win projects.

Conclusion

  1. Summary: Reiteration of exam sections and study focus areas.
  2. Looking Ahead: Upcoming video lessons on detailed subject matter: planning data analytics solution, managing data sequence, data transformation, performance optimization, designing semantic models, advanced data analytics.
  3. Final Notes: Importance of certifications in career advancements, consultancy benefits.

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