Microsoft DP-600: Implementing Analytics Solutions Using Microsoft Fabric eLearning

COURSE DESCRIPTION

This course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will build on existing analytics experience and will learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets.

This course is best suited for those who have the PL-300 certification or similar expertise in using Power BI for data transformation, modeling, visualization, and sharing. Also, learners should have prior experience in building and deploying data analytics solutions at the enterprise level.

• 12 Months Online Access
• 26+ hours of eLearning
• Measure-Up Exam simulation – 120 questions
• Tips & Tricks

CERTIFICATION

This course prepares participants for the official Microsoft DP-600 exam, which can be taken separately after completing the training. This exam is optional and can be ordered separately with the training course.

LEARNING OUTCOMES

In this course, you will learn to:

• Get started with Microsoft Fabric
• Implement a Lakehouse with Microsoft Fabric
• Ingest data with Microsoft Fabric
• Implement a data warehouse with Microsoft Fabric
• Work with semantic models in Microsoft Fabric

TARGET AUDIENCE

The primary audience for this course is data professionals with experience in data modeling and analytics. DP-600 is designed for professionals who want to use Microsoft Fabric to create and deploy enterprise-scale data analytics solutions.

CONTENT

• Getting Started and the DP-600 Exam
• Introducing Data Warehouses
• Monitoring Fabric Warehouses
• Implementing Security in Fabric Warehouses
• Getting Started with Semantic Models
• Semantic Models and Semantic Link
• Getting Started with DAX Queries
• Advanced DAX Queries and Tools to Manage Semantic Models
• Administration, T-SQL, and Data Analytics
• OneLake, KQL, and KQL DB
• Working with Data Lakehouses
• Spark & the Capacity Metrics App for Lakehouses
• Spark Configuration & Delta Tables
• The Medallion Architecture with a Star Schema
• Data Transformations Using Dataflow Gen2
• Creating and Using Simple Data Pipelines
• Working with Complex Data Pipelines

Learners should have prior experience with one of the following programming languages: Structured Query Language (SQL), Kusto Query Language (KQL), or Data Analysis Expressions (DAX).