Dp-600: Microsoft Fabric Analytics Engineer Associate 2025
Published 6/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.46 GB | Duration: 5h 46m
Published 6/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.46 GB | Duration: 5h 46m
15+ REAL End-to-End Fabric Analytic Engineer Projects | 73+ High-Quality Practice Exam Questions with VIDEO Explanation!
What you'll learn
Build hands-on solutions using Fabric Workspace, Lakehouse, and SQL Analytics Endpoints.
Design Fact & Dimension tables and create efficient Data Models for analytics.
Analyze performance using Dynamic Management Views (DMVs) and Query Insights.
Validate business data using Data Consistency checks in real-world scenarios.
Implement Dynamic Data Masking, Row-Level & Column-Level Security in Fabric.
Ingest and analyze real-time stock data using Eventstream and Eventhouse.
Train machine learning models on diabetic patient data for predictive insights.
Create advanced visualizations using Data Science Notebooks in Fabric.
Manage project tracking, issues, and dashboards with Fabric tools.
Develop CI/CD pipelines for continuous analytics deployment using Fabric.
Build and query APIs using GraphQL for real-time analytics and integration.
Implement Slowly Changing Dimensions (SCD) Type 1 and Type 2 in Lakehouse.
Configure Dynamic Data Masking in Azure SQL for sensitive data protection.
Create and manage Azure SQL Databases and Azure SQL Servers efficiently.
Query and visualize big data using Kusto Query Language in Azure Explorer.
Master SQL & Visual Query Editors to build and debug optimized queries.
Understand Delta Lake Tables and their role in transactional data lakes.
Work with PySpark DataFrames and Spark SQL to handle big data efficiently.
Distinguish between types of analytics and apply appropriate techniques.
Learn data warehousing principles and how Fabric fits into modern BI.
Master use of notebooks, dataframes, and scripting for analytics modeling.
Understand API architecture using GraphQL and implement it in Fabric.
Deep dive into theory behind SCDs and implement use cases in the cloud.
Practice with exam questions and understand Microsoft-backed answer logic.
Requirements
Familiarity with basic SQL concepts like SELECT, JOIN, and WHERE clauses.
Awareness of data warehousing and analytics concepts.
Knowledge of Python basics
Prior exposure to Azure fundamentals
Description
Looking to ace the DP-600 exam and gain practical, real-world experience with Microsoft Fabric? This course offers a complete hands-on journey designed around projects that reflect real IT industry scenarios using Microsoft Fabric's modern analytics stack.Each module begins with theory-first learning—covering essential concepts like Fabric Workspace, Lakehouse architecture, SQL Analytics Endpoints, Delta Tables, PySpark DataFrames, Spark SQL, Visual Query Editors, Eventhouses, SCD Types, and more. Then, you’ll dive into realistic, guided labs that strengthen technical skills while preparing you for certification success.End-to-End Hands-on Projects Included:Project #1: Fabric Workspace, Lakehouse & SQL Analytics Endpoint SetupProject #2: Designing Fact Tables, Dimension Tables & Data ModelsProject #3: Performance Tuning with Dynamic Management Views (DMV) & Query InsightsProject #4: Validating Data Consistency Across SourcesProject #5: Implementing Dynamic Data Masking in Azure SQL DatabaseProject #6: Securing Data with Row-Level & Column-Level Security in FabricProject #7: Real-Time Stock Data Ingestion & Analysis Using EventstreamProject #8: Diabetic Patient Data Analysis & Predictive Model TrainingProject #9: Data Science Notebook for Mastering VisualizationsProject #10: Building a Project Tracking & Issue Management SystemProject #11: CI/CD Deployment Pipeline for Fabric Analytics SolutionsProject #12: Creating & Consuming an API with GraphQLProject #13: SCD Type 1 & SCD Type 2 Implementation in FabricProject #14: Azure SQL Server & Database Configuration with Data MaskingProject #15: Azure Data Explorer – Kusto Query Language (KQL) Hands-on DemoYou’ll also gain access to interactive practice tests with detailed video explanations for both correct and incorrect choices—rooted in Microsoft documentation and explained using strategic elimination techniques.This is not just another course—it’s your launchpad to becoming a confident, job-ready Fabric Analytics Engineer, taught by Cloud Guru Amit. Get certified, get skilled, and get ahead.
Overview
Section 1: DP-600: Fabric Analytics Engineer Associate Theory & End-to-End Hands-on Project
Lecture 1 How to create Microsoft Fabric Account
Lecture 2 What is SQL Query Editor & Visual Query Editor
Lecture 3 What is Delta Lake Table
Lecture 4 Pyspark Dataframes
Lecture 5 Commonly used Spark SQL Commands
Lecture 6 Types of Analytics
Lecture 7 What is Fabric Workspace, Lakehouse & SQL Analytics Endpoint
Lecture 8 Fabric Workspace, Lakehouse & SQL Analytics Endpoint Hands-on Project #1
Lecture 9 What is Fact, Dimension Table & Data Model
Lecture 10 Fact Table, Dimension Table & Data Model Hands-on Project #2
Lecture 11 What is Data Warehouse, Dynamic Management Views (DMV) & Query Insights
Lecture 12 Dynamic Management Views (DMV) & Query Insights Hands-on Project #3
Lecture 13 What is Data Consistency
Lecture 14 Data Consistency Validation Hands-on Project #4
Lecture 15 What is Dynamic Data Masking, Row Level Security & Column Level Security
Lecture 16 Dynamic Data Masking, Row Level & Column Level Security Hands-on Project #5
Lecture 17 What is Eventhouse & Eventstream
Lecture 18 Real-Time Stock Data Ingestion & Analysis using Eventstream Hands-on Project #6
Lecture 19 What is Notebook and Dataframe
Lecture 20 Diabetic Patient Data Analysis & Model Training Hands-on Project #7
Lecture 21 Master Data Visualizations using Data Science Notebook Hands-on Project #8
Lecture 22 Project Tracking & Issue Management Hands-on Project #9
Lecture 23 What is Deployment Pipeline
Lecture 24 CI/CD Deployment Pipeline Hands-on Project #10
Lecture 25 What is API for GraphQL
Lecture 26 API for GraphQL Hands-on Project #11
Lecture 27 Implementing Dynamic Data Masking in Azure SQL Database Hands-on Project #12
Lecture 28 Kusto Query Language (KQL) basics using Azure Data Explorer Hands-on Project #13
Lecture 29 What is SCD Type 1 & SCD Type 2
Lecture 30 SCD Type 1 & SCD Type 2 Hands-on Project #14
Section 2: DP-600: Fabric Analytics Engineer Associate Practice Test Questions
Lecture 31 DP-600: Fabric Analytics Engineer Associate Practice Test-1
Lecture 32 DP-600: Fabric Analytics Engineer Associate Practice Test-2
Lecture 33 DP-600: Fabric Analytics Engineer Associate Practice Test-3
Lecture 34 DP-600: Fabric Analytics Engineer Associate Practice Test-4
Lecture 35 DP-600: Fabric Analytics Engineer Associate Practice Test-5
Lecture 36 DP-600: Fabric Analytics Engineer Associate Practice Test-6
Lecture 37 DP-600: Fabric Analytics Engineer Associate Practice Test-7
Lecture 38 DP-600: Fabric Analytics Engineer Associate Practice Test-8
Lecture 39 DP-600: Fabric Analytics Engineer Associate Practice Test-9
Lecture 40 DP-600: Fabric Analytics Engineer Associate Practice Test-10
Lecture 41 DP-600: Fabric Analytics Engineer Associate Practice Test-11
Lecture 42 DP-600: Fabric Analytics Engineer Associate Practice Test-12
Lecture 43 DP-600: Fabric Analytics Engineer Associate Practice Test-13
Lecture 44 DP-600: Fabric Analytics Engineer Associate Practice Test-14
Lecture 45 DP-600: Fabric Analytics Engineer Associate Practice Test-15
Aspiring Data Analysts, Engineers, and BI Developers looking to gain hands-on expertise with Microsoft Fabric and its analytics ecosystem.,IT professionals and database administrators seeking to upskill in Fabric Workspace, Lakehouse, and SQL Analytics Endpoints.,Candidates preparing for the DP-600 certification exam who want real-world, project-based learning to master the exam objectives.,Cloud and Data Engineers aiming to integrate Fabric tools with Azure services like Eventstream, SQL Database, and Data Explorer.,Developers and Architects interested in securing analytics solutions using Row-Level/Column-Level Security and Dynamic Data Masking.,Python or PySpark learners wanting to apply their skills in data science notebooks, model training, and data visualizations in Fabric.,Data-driven professionals who value understanding concepts deeply—like SCDs, DMVs, Delta Lake, GraphQL APIs—backed by hands-on execution.