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Implement analytics solutions using Microsoft Fabric

This course covers how to prepare, enrich, and serve data for analysis by consumers such as data analysts, report developers, and AI agents. The course focuses on designing dimensional models and...

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$2,495 USD
Duration 4 days
Course Code DP-600T00
Available Formats Classroom, Virtual

Upcoming Course Dates

  • Jul 27, 2026 - Jul 30, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EDT
    Language English
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    Live Virtual | 9:30AM – 5:30PM EDT
    Live Virtual | 9:30AM – 5:30PM EDT
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  • Sep 28, 2026 - Oct 1, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EDT
    Language English
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    Live Virtual | 9:30AM – 5:30PM EDT
    Live Virtual | 9:30AM – 5:30PM EDT
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  • Nov 9, 2026 - Nov 12, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EST
    Language English
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    Live Virtual | 9:30AM – 5:30PM EST
    Live Virtual | 9:30AM – 5:30PM EST
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Overview

Course Description

This course covers how to prepare, enrich, and serve data for analysis by consumers such as data analysts, report developers, and AI agents. The course focuses on designing dimensional models and transforming data by using dataflows, notebooks, and T-SQL across lakehouses, warehouses, and eventhouses in Microsoft Fabric. The course also covers building and optimizing semantic models, managing the analytics development lifecycle, and enforcing security and governance across data assets.

Audience Profile

This course is intended for data professionals with experience in data modeling, transformation, and analytics. Learners should have prior experience translating business requirements into analytical measures by using Structured Query Language (SQL) or Data Analysis Expressions (DAX). Experience building semantic models and reports in Power BI is recommended. Familiarity with Kusto Query Language (KQL) and Python is also helpful but not required.

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Course Details

Course Details

Outline

  • Introduction to end-to-end analytics using Microsoft Fabric
    • Explore end-to-end analytics with Microsoft Fabric
    • Explore data teams and Microsoft Fabric
    • Enable and use Microsoft Fabric
    • Module assessment
  • Discover and connect to data in OneLake
    • Understand OneLake
    • Browse and connect to data in OneLake
    • Discover streaming data in Real-Time hub
    • Exercise - Discover and connect to data in OneLake
    • Knowledge check
  • Get started with lakehouses in Microsoft Fabric
    • Describe lakehouse features and capabilities
    • Ingest and transform data in a lakehouse
    • Query and analyze lakehouse data
    • Exercise - Create a Microsoft Fabric lakehouse
    • Module assessment
  • Get started with data warehouses in Microsoft Fabric
    • Understand data warehouses
    • Understand data warehouses in Fabric
    • Query and transform data
    • Model data in a warehouse
    • Secure and monitor a warehouse
    • Exercise - Create and query a warehouse
    • Module assessment
  • Get started with Real-Time Intelligence in Microsoft Fabric
    • What is real-time data analytics?
    • Real-Time Intelligence in Microsoft Fabric
    • Ingest and transform real-time data
    • Store and query real-time data
    • Visualize real-time data
    • Automate actions
    • Exercise - Get started with Real-Time Intelligence in Microsoft Fabric
    • Module assessment
  • Choose data stores in Microsoft Fabric
    • Describe analytical data store options
    • Evaluate lakehouse capabilities
    • Evaluate warehouse capabilities
    • Evaluate eventhouse capabilities
    • Case study - Choose data stores for an integrated analytics solution
    • Module assessment
  • Design dimensional models for analytics in Microsoft Fabric
    • Describe dimensional schema types
    • Design fact tables
    • Design dimension tables
    • Implement slowly changing dimensions
    • Exercise: Design and implement a dimensional model
    • Knowledge check
  • Transform data using Dataflows Gen2 in Microsoft Fabric
    • Understand Dataflows Gen2
    • Transform data with Power Query
    • Optimize Dataflows Gen2 performance
    • Exercise: Transform data with Dataflows Gen2
    • Knowledge check
  • Transform data using notebooks in Microsoft Fabric
    • Describe notebooks in Fabric
    • Shape and clean data
    • Combine and aggregate data
    • Write and size Delta tables
    • Exercise: Transform data with notebooks
    • Knowledge check
  • Transform data using T-SQL in Microsoft Fabric
    • Transform data with T-SQL queries
    • Create views for reusable logic
    • Build stored procedures
    • Implement dimensional tables
    • Exercise: Transform data with T-SQL
    • Knowledge check
  • Create DAX calculations in semantic models
    • Create calculated tables
    • Create calculated columns
    • Understand implicit measures
    • Create explicit measures
    • Use iterator functions
    • Exercise - Create DAX calculations
    • Check your knowledge
  • Design semantic models for scale in Microsoft Fabric
    • Choose a storage mode
    • Design star schema for semantic models
    • Design scalable calculations
    • Configure settings for scale
    • Exercise: Design a semantic model for scale in Fabric
    • Module assessment
  • Optimize semantic model performance
    • Use Performance analyzer to diagnose issues
    • Optimize DAX calculations
    • Reduce cardinality for better performance
    • Implement aggregations
    • Troubleshoot common performance issues
    • Exercise: Diagnose and fix a slow report
    • Knowledge check
  • Enforce semantic model security
    • Implement row-level security
    • Apply object-level security
    • Test security and manage roles
    • Exercise: Implement RLS for a semantic model
    • Module assessment
  • Manage the semantic model development lifecycle
    • Create reusable Power BI assets
    • Manage Power BI content in version control
    • Manage semantic models with the XMLA endpoint
    • Deploy content through stages
    • Maintain and monitor semantic models
    • Exercise: Manage semantic models through their lifecycle
    • Module assessment
  • Prepare the semantic layer for AI in Microsoft Fabric
    • Understand what AI needs from your data
    • Design gold layers with AI in mind
    • Prepare a semantic model for AI
    • From semantic models to enterprise ontology
    • Validate AI readiness
    • Exercise: Prepare a semantic model for AI
    • Module assessment
  • Understand Microsoft Fabric IQ fundamentals
    • Get started with Fabric IQ
    • Explore Microsoft Fabric IQ components
    • Understand the ontology modeling paradigm
    • Module assessment
  • Create an ontology with Fabric IQ
    • Choose an ontology creation approach
    • Build an ontology manually
    • Generate an ontology from a Power BI semantic model
    • Connect an ontology to data
    • Configure ontology relationships
    • Preview the ontology
    • Exercise - Build an ontology manually
    • Exercise - Generate an ontology from a Power BI semantic model
    • Module assessment
  • Secure data access in Microsoft Fabric
    • Understand the Fabric security model
    • Configure workspace and item permissions
    • Apply granular permissions
    • Exercise: Secure data access in Microsoft Fabric
    • Module assessment
  • Secure a Microsoft Fabric data warehouse
    • Explore dynamic data masking
    • Implement row-level security
    • Implement column-level security
    • Configure SQL granular permissions using T-SQL
    • Exercise: Secure a warehouse in Microsoft Fabric
    • Module assessment
  • Govern data in Microsoft Fabric with Purview
    • Govern data in Microsoft Fabric
    • Why use Microsoft Purview with Microsoft Fabric?
    • Govern data in the Microsoft Purview hub
    • Module assessment
  • Govern analytics data in Microsoft Fabric
    • Classify and protect data in Microsoft Fabric
    • Endorse and document data assets
    • Govern data for AI consumption
    • Exercise - Govern analytics data in Microsoft Fabric
    • Knowledge check

Schedule

3 options available

  • Jul 27, 2026 - Jul 30, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EDT
    Language English
    Select from 1 option(s) below
    Live Virtual | 9:30AM – 5:30PM EDT
    Live Virtual | 9:30AM – 5:30PM EDT
    Enroll
    Enroll Add to quote
  • Sep 28, 2026 - Oct 1, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EDT
    Language English
    Select from 1 option(s) below
    Live Virtual | 9:30AM – 5:30PM EDT
    Live Virtual | 9:30AM – 5:30PM EDT
    Enroll
    Enroll Add to quote
  • Nov 9, 2026 - Nov 12, 2026 (4 days)
    Live Virtual | 9:30AM – 5:30PM EST
    Language English
    Select from 1 option(s) below
    Live Virtual | 9:30AM – 5:30PM EST
    Live Virtual | 9:30AM – 5:30PM EST
    Enroll
    Enroll Add to quote

FAQ

How do I get a Microsoft exam voucher?

Pearson Vue Exam vouchers can be requested and ordered with your course purchase.

  • Vouchers are non-refundable and non-returnable. Vouchers expire 12 months from the date they are issued unless otherwise specified in the terms and conditions.
  • Voucher expiration dates cannot be extended. The exam must be taken by the expiration date printed on the voucher.

Do Microsoft courses come with post lab access?

Most Microsoft official courses will include post-lab access ranging from 30 to 180 calendar days after instructor led course delivery. A lab training key in class will be provided that can be leveraged to continue connecting to a remote lab environment for the individual course attendee.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour-long after 3-3.5 hours of the class day.

What languages are used to deliver training?

Microsoft courses are conducted in English unless otherwise specified.

Reviews

Course was great and the instructor had an answer for anything that was asked during the course.

ExitCertified provided great learning material and the instructor was great.

Overall experiance is very nice. the online training plateform is very advance.

Instructor, Training material & span of the training is neatly planned.

Great instructor, clear and concise course. Labs were easy to follow and worked perfectly.