training
Microsoft Fabric Analytics Engineer (DP-600)
Ontwerp, transformeer en lever data voor analyse op schaal
Beschrijving
Deze training behandelt hoe u gegevens voorbereidt, verrijkt en beschikbaar maakt voor analyse door gebruikers zoals data-analisten, rapportontwikkelaars en AI-agenten. De training richt zich op het ontwerpen van dimensionale modellen en het transformeren van gegevens met behulp van dataflows, notebooks en T-SQL in lakehouses, warehouses en eventhouses in Microsoft Fabric. Daarnaast behandelt de training het bouwen en optimaliseren van semantische modellen, het beheren van de levenscyclus van analyticsontwikkeling en het afdwingen van beveiliging en governance voor data-assets.
Doelgroep
Deze training is bedoeld voor dataprofessionals met ervaring in datamodellering, transformatie en analytics. Deelnemers moeten ervaring hebben met het vertalen van bedrijfsvereisten naar analytische metingen met behulp van Structured Query Language (SQL) of Data Analysis Expressions (DAX). Ervaring met het bouwen van semantische modellen en rapporten in Power BI wordt aanbevolen. Bekendheid met Kusto Query Language (KQL) en Python is nuttig, maar niet vereist.
Certificering
Deze training bereidt je voor op het Associate-examen DP-600 (Microsoft Certified: Fabric Analytics Engineer Associate)
Leerdoelen
 Identify the key characteristics of OneLake, lakehouses, warehouses, and eventhouses in Microsoft Fabric. | Remember  |
| Explain end-to-end analytics in Microsoft Fabric, from data ingestion to reporting and analysis consumption. | Understand  |
| Explain how Fabric data stores differ in workload type, latency, data structure, and query patterns. | Understand |
| Apply dimensional modeling principles to design a practical star schema for analytics workloads. | Apply  |
| Implement data transformations with Dataflows Gen2, notebooks, and T-SQL to deliver consistent, analysis-ready data. | Apply |
| Demonstrate DAX calculations, relationships, and foundational measure logic in a semantic model. | Apply |
| Explain how to improve semantic model scalability and performance using targeted optimization techniques. | Understand |
| Describe the metadata and semantic context required to make analytics data AI-ready for Fabric IQ. | Understand |
| Use a controlled semantic model development lifecycle focused on change management and release practices. | Apply |
| Describe security and governance controls across workspace, data store, and semantic model layers. | Understand |
Benodigde voorkennis
Ervaring met:
- Microsoft Power BI Power BI Data Analyst
- Querying T-SQL Core Querying Data with T-SQL
- Basic Python Essentials of Python Development
Onderwerpen
- Explore analytics data stores in Microsoft Fabric
- Design and transform analytics data in Microsoft Fabric
- Design and manage semantic models in Microsoft Fabric
- Prepare AI-ready analytics data in Microsoft Fabric
- Secure and govern analytics data in Microsoft Fabric
1: Explore analytics data stores in Microsoft Fabric
Understand how Microsoft Fabric unifies analytics storage through OneLake, and evaluate when to use lakehouses, warehouses, or eventhouses for your data workloads.
Lessons
- Introduction to end-to-end analytics using Microsoft Fabric
- Discover and connect to data in OneLake
- Get started with lakehouses in Microsoft Fabric
- Get started with data warehouses in Microsoft Fabric
- Get started with Real-Time Intelligence in Microsoft Fabric
2: Design and transform analytics data in Microsoft Fabric
Design dimensional models and apply transformations using dataflows, Spark notebooks, and T-SQL to produce consistent, analysis-ready data in Microsoft Fabric.
Lessons
- Choose data stores in Microsoft Fabric
- Design dimensional models for analytics in Microsoft Fabric
- Transform data using Dataflows Gen2 in Microsoft Fabric
- Transform data using notebooks in Microsoft Fabric
- Transform data using T-SQL in Microsoft Fabric
3: Design and manage semantic models in Microsoft Fabric
Create semantic models that serve reliable, governed analytics at scale — from initial DAX logic through performance optimization, data access control, and automated lifecycle management.
Lessons
- Create DAX calculations in semantic models
- Design semantic models for scale in Microsoft Fabric
- Optimize semantic model performance
- Enforce semantic model security
- Manage the semantic model development lifecycle
4: Prepare AI-ready analytics data in Microsoft Fabric
Prepare your semantic layer for AI by adding metadata and linguistic context to gold-layer data stores and semantic models, then generate ontologies that AI agents use to answer business questions.
Lessons
- Prepare the semantic layer for AI in Microsoft Fabric
- Understand Microsoft Fabric IQ fundamentals
- Create an ontology with Fabric IQ
5: Secure and govern analytics data in Microsoft Fabric
Implement layered access controls across Microsoft Fabric workspaces and data stores, then establish governance policies that help consumers identify trustworthy, certified analytics assets.
Lessons
- Secure data access in Microsoft Fabric
- Secure a Microsoft Fabric data warehouse
- Govern analytics data in Microsoft Fabric
Planning
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Trainers
Voorkennis trainingen
Microsoft Power BI Data Analyst (PL-300)
Bouw snelle, effectieve en interactieve visuals, rapportages en dashboards om je data te analyseren
- Data Analytics
T-SQL Querying Fundamentals (DP-080)
Leer queries te schrijven voor SQL Server en Azure data sources
- Databases
Python Fundamentals
Vorm een solide basis om software te ontwikkelen in Python
- Python
Certificering
De kennis uit deze training sluit aan bij deze certificeringen.
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