Microsoft Fabric Analytics Engineer (DP-600) - Fact Sheet¶

Quick Reference¶

Exam Code: DP-600 Duration: 120 minutes Questions: 40-60 questions Passing Score: 700/1000 Cost: $165 USD Validity: 3 years Difficulty: ⭐⭐⭐⭐

Exam Domains¶

Microsoft Fabric Overview¶

What is Microsoft Fabric? - Unified analytics platform (SaaS) - Combines Data Engineering, Data Science, Data Warehousing, Real-time Analytics, Power BI - Built on OneLake (unified data lake) - Single capacity-based pricing model - 📖 Microsoft Fabric Documentation - Complete Fabric guide - 📖 Fabric Get Started - Platform overview - 📖 Fabric Architecture - Technical architecture - 📖 Fabric Licensing - Capacity and licensing

OneLake¶

OneLake: Unified Data Lake - Single, hierarchical namespace - ADLS Gen2 compatible - Automatic data organization - Delta Lake format by default - Shortcuts: Access data without copying - 📖 OneLake Overview - OneLake concepts - 📖 OneLake Data Hub - Data discovery - 📖 OneLake Shortcuts - Data federation - 📖 OneLake Security - Access control - 📖 OneLake Integration - Azure Storage integration

Fabric Workspaces¶

Workspaces - Containers for Fabric items - Role-based access: Admin, Member, Contributor, Viewer - Workspace capacity assignment - Licensing modes: Trial, Premium, Fabric - 📖 Workspaces - Workspace management - 📖 Workspace Roles - Permission levels - 📖 Workspace Identity - Service principal access

Lakehouses¶

Lakehouse Architecture - Combines data lake (Parquet) and warehouse (SQL endpoint) - Delta Lake format for ACID transactions - Automatic metadata generation - SQL analytics endpoint (read-only) - Notebooks for data engineering - 📖 Lakehouse Overview - Lakehouse concepts - 📖 Create Lakehouse - Setup guide - 📖 Lakehouse SQL Endpoint - SQL analytics - 📖 Tables in Lakehouse - Table management - 📖 Lakehouse Files - File management - 📖 V-Order Optimization - Performance optimization

Data Factory in Fabric¶

Data Pipelines - Copy data activity - Dataflow Gen2 for transformations - Pipeline orchestration - Schedule and triggers - Integration with Azure Data Factory - 📖 Data Factory Overview - Pipeline concepts - 📖 Copy Activity - Data ingestion - 📖 Dataflow Gen2 - Data transformation - 📖 Pipeline Activities - Activity reference - 📖 Pipeline Monitoring - Run monitoring - 📖 Pipeline Parameters - Parameterization

Notebooks and Spark¶

Fabric Notebooks - Python, Scala, R, SQL languages - Apache Spark engine - Built-in data visualization - Integration with lakehouses - Notebook scheduling - 📖 Notebooks Overview - Notebook guide - 📖 Spark Compute - Spark configurations - 📖 Notebook Source Control - Git integration - 📖 PySpark Reference - PySpark tutorial - 📖 Delta Lake with Spark - Delta operations

Data Warehouse¶

Fabric Warehouse - T-SQL analytics - Columnar storage - Separation of storage and compute - Automatic query optimization - Cross-database queries - 📖 Warehouse Overview - Warehouse concepts - 📖 Create Warehouse - Setup guide - 📖 Warehouse Tables - Table design - 📖 Warehouse Ingestion - Data loading - 📖 Query Warehouse - T-SQL queries - 📖 Warehouse Security - Row-level security

Semantic Models (Power BI Datasets)¶

Data Modeling - Import, DirectQuery, Composite modes - Star schema design - Relationships and cardinality - Calculated columns and measures - Hierarchies and display folders - 📖 Semantic Models Overview - Modeling concepts - 📖 DirectLake - Direct Lake mode - 📖 Data Modeling Best Practices - Star schema design - 📖 Relationships - Relationship types - 📖 Calculated Columns vs Measures - Calculations - 📖 Model Optimization - Performance tuning

DAX (Data Analysis Expressions)¶

DAX Functions and Patterns - Aggregation: SUM, AVERAGE, COUNT, MIN, MAX - Filter context: CALCULATE, FILTER, ALL, ALLEXCEPT - Time intelligence: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD - Iterators: SUMX, AVERAGEX, COUNTX - Relationship navigation: RELATED, RELATEDTABLE - 📖 DAX Reference - Complete DAX reference - 📖 CALCULATE Function - Context transition - 📖 Time Intelligence - Date functions - 📖 DAX Best Practices - Performance patterns - 📖 Variables in DAX - VAR keyword

Real-Time Analytics (KQL Database)¶

Kusto Query Language (KQL) - Time-series analytics - Streaming data ingestion - Event-based data - Fast queries on large datasets - Integration with Event Hubs, IoT Hub - 📖 KQL Database Overview - KQL database - 📖 KQL Query Language - Query syntax - 📖 Data Ingestion - Eventstreams - 📖 KQL Queryset - Query management - 📖 Real-Time Dashboards - Dashboard creation

Power BI Integration¶

Reports and Dashboards - Report design and visualization - Paginated reports - Real-time streaming - Row-level security (RLS) - Apps and content distribution - 📖 Power BI in Fabric - Power BI integration - 📖 Create Reports - Report design - 📖 Visualizations - Visual types - 📖 Row-Level Security - RLS implementation - 📖 Paginated Reports - Report Builder - 📖 Power BI Apps - App deployment

Data Science in Fabric¶

Machine Learning - ML models with notebooks - MLflow integration - Model training and tracking - Model deployment - 📖 Data Science Overview - ML in Fabric - 📖 Train Models - Model training - 📖 MLflow - Experiment tracking - 📖 Model Registry - Model management

Security and Governance¶

Access Control - Workspace roles and permissions - Item permissions - Row-level security (RLS) - Object-level security (OLS) - Dynamic data masking - 📖 Fabric Security - Security overview - 📖 Workspace Permissions - Access control - 📖 Data Loss Prevention - Data protection - 📖 Endorsement - Content certification - 📖 Sensitivity Labels - Data classification

Monitoring and Optimization¶

Performance Monitoring - Monitoring hub - Capacity metrics app - Query performance analysis - Usage metrics - Diagnostic logs - 📖 Monitoring Hub - Centralized monitoring - 📖 Capacity Metrics - Capacity analytics - 📖 Query Insights - Query performance - 📖 Performance Analyzer - Report optimization

Data Integration Patterns¶

Common Patterns - Medallion architecture (Bronze, Silver, Gold) - Lambda architecture (batch + streaming) - Hub-and-spoke data distribution - Data mesh with domains - ELT over ETL - 📖 Medallion Architecture - Layered approach - 📖 Data Pipelines Best Practices - Design patterns

Migration to Fabric¶

Migration Strategies - Power BI workspace migration - Azure Synapse to Fabric - Azure Data Factory to Fabric Data Factory - On-premises data sources - 📖 Migrate to Fabric - Migration guide - 📖 Synapse Migration - Synapse to Fabric

Common Scenarios¶

Scenario 1: Modern Data Warehouse - Solution: Lakehouse → Warehouse → Semantic Model → Power BI Reports

Scenario 2: Real-Time Analytics Dashboard - Solution: Eventstream → KQL Database → Real-Time Dashboard

Scenario 3: Self-Service BI - Solution: Lakehouse → DirectLake Semantic Model → Power BI Reports

Scenario 4: Data Engineering Pipeline - Solution: Source → Data Pipeline → Dataflow Gen2 → Lakehouse (Bronze/Silver/Gold)

Scenario 5: ML Model Deployment - Solution: Lakehouse → Notebook → MLflow → Model Registry → Batch Scoring

Essential Commands¶

Python (PySpark) in Notebooks:

# Read from lakehouse
df = spark.read.format("delta").load("Tables/tablename")

# Write to lakehouse
df.write.format("delta").mode("overwrite").save("Tables/tablename")

# Optimize Delta table
spark.sql("OPTIMIZE tablename")

# V-Order write
df.write.format("delta").option("optimizeWrite", "true").save("Tables/tablename")

KQL Queries:

// Time-series aggregation
TableName
| where Timestamp > ago(1h)
| summarize Count=count() by bin(Timestamp, 5m)
| render timechart

// Top N analysis
TableName
| summarize Total=sum(Amount) by Category
| top 10 by Total desc

DAX Measures:

// Time intelligence
Sales YTD = TOTALYTD(SUM(Sales[Amount]), Dates[Date])

// Context modification
Sales All Regions = CALCULATE(SUM(Sales[Amount]), ALL(Region))

Exam Tips¶

Keywords: - "Unified analytics" → Microsoft Fabric - "Single data lake" → OneLake - "ACID transactions on data lake" → Delta Lake format - "No data movement" → Shortcuts - "Fast semantic model" → DirectLake mode - "Real-time analytics" → KQL Database, Eventstream - "T-SQL analytics" → Warehouse - "Python/Spark transformations" → Notebooks - "Low-code ETL" → Dataflow Gen2

Focus Areas: - Understand when to use Lakehouse vs Warehouse - DirectLake mode and its benefits - OneLake shortcuts for data federation - Medallion architecture (Bronze, Silver, Gold layers) - DAX context (filter context vs row context) - KQL query syntax for time-series data - Workspace roles and permissions - Capacity management and optimization - Data modeling best practices

Study Strategy: - Hands-on: Create lakehouses, build data pipelines, design semantic models - Practice DAX and KQL queries - Understand DirectLake vs DirectQuery vs Import - Know the Fabric item types and their use cases - Memorize capacity SKUs and limits

Pro Tip: Microsoft Fabric is relatively new (GA November 2023). Focus on understanding the unified architecture, OneLake concepts, and how traditional Power BI/Synapse/Data Factory concepts map to Fabric!