The Microsoft DP-600 certification exam validates the skills of professionals who specialize in building, managing, and optimizing analytical assets within the Microsoft Fabric ecosystem. These assets may include semantic models, data warehouses, and lakehouses, all of which play a vital role in enterprise-level data analytics. However, certified professionals in this role are expected to:

• Prepare and Transform Data for Analysis
  • Ingest, cleanse, and shape data to make it analysis-ready, ensuring it meets business requirements.
• Secure and Maintain Analytical Assets
  • Implement governance, access control, and maintenance strategies to safeguard data assets.
• Develop and Manage Semantic Models
  • Design and implement robust semantic layers to support self-service and enterprise reporting needs.

– Collaboration and Stakeholder Engagement

Professionals in this role frequently collaborate with various stakeholders, including business analysts, architects, data engineers, and system administrators, to align technical implementations with organizational objectives.

– Technical Skill Requirements

Candidates should possess hands-on expertise in querying and analyzing data using:

• SQL (Structured Query Language) for relational data sources
• KQL (Kusto Query Language) for telemetry and log data
• DAX (Data Analysis Expressions) for building measures and calculated columns in semantic models

Exam Details

• The DP-600 certification exam is designed for individuals at the intermediate level who work in roles such as Data Engineers or Data Analysts.
• Candidates are given 100 minutes to complete the assessment, which is proctored and may include interactive tasks to evaluate hands-on skills.
• The exam is available in multiple languages, including English, Japanese, Simplified Chinese, German, French, Spanish, and Brazilian Portuguese.
• To pass the exam, a minimum score of 700 is required.
• Microsoft also offers accommodations for individuals who use assistive technologies, require additional time, or need adjustments to any part of the exam experience.
• These accommodations can be requested in advance to ensure a fair testing environment.

Course Outline

The exam covers the following topics:

1. Maintaining a data analytics solution (25–30%)

Implementing security and governance

• Implementing workspace-level access controls
• Implementing item-level access controls
• Implementing row-level, column-level, object-level, and file-level access control
• Applying sensitivity labels to items (Microsoft Documentation: Learn about sensitivity labels)
• Endorse items

Maintaining the analytics development lifecycle

• Configuring version control for a workspace (Microsoft Documentation: Version control, metadata search, and navigation)
• Creating and managing a Power BI Desktop project (.pbip) (Microsoft Documentation: Power BI Desktop projects (PREVIEW))
• Creating and configuring deployment pipelines (Microsoft Documentation: Planning the Deployment)
• Performing impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models (Microsoft Documentation: Semantic model impact analysis)
• Deploying and managing semantic models by using the XMLA endpoint (Microsoft Documentation: Semantic model connectivity with the XMLA endpoint)
• Creating and updating reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models (Microsoft Documentation: Create and use report templates in Power BI Desktop, Semantic models in the Power BI service)

2. Preparing data (45–50%)

Get data

• Creating a data connection
• Discovering data by using OneLake data hub and real-time hub
• Ingest or access data as needed
• Choosing between a lakehouse, warehouse, or eventhouse
• Implementing OneLake integration for eventhouse and semantic models

Transforming data

• Create views, functions, and stored procedures
• Enriching data by adding new columns or tables (Microsoft Documentation: Data collection transformations in Azure Monitor)
• Implementing a star schema for a lakehouse or warehouse (Microsoft Documentation: Understand star schema and the importance for Power BI)
• Denormalizing data (Microsoft Documentation: Modeling for Performance)
• Aggregating data (Microsoft Documentation: User-defined aggregations)
• Merging or joining data (Microsoft Documentation: Merge queries (Power Query))
• Identifying and resolving duplicate data, missing data, or null values (Microsoft Documentation: Set up duplicate detection rules to keep your data clean)
• Convert column data types
• Filtering data

Querying and analyzing data

• Selecting, filtering, and aggregating data by using the Visual Query Editor
• Select, filter, and aggregate data by using SQL
• Selecting, filtering, and aggregating data by using KQL

3. Implementing and managing semantic models (25–30%)

Designing and building semantic models

• Choosing a storage mode
• Implementing a star schema for a semantic model (Microsoft Documentation: Understand star schema and the importance for Power BI)
• Implementing relationships, such as bridge tables and many-to-many relationships (Microsoft Documentation: Many-to-many relationship guidance)
• Writing calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions (Microsoft Documentation: Use variables to improve your DAX formulas)
• Implementing calculation groups, dynamic format strings, and field parameters (Microsoft Documentation: Calculation groups)
• Designing and building a large format dataset (Microsoft Documentation: Datasets larger than 10 GB in Power BI Premium)
• Identifying use cases for and configure large semantic model storage format
• Designing and building composite models (Microsoft Documentation: Use composite models in Power BI Desktop)

Optimizing enterprise-scale semantic models

• Implementing performance improvements in queries and report visuals (Microsoft Documentation: Optimization guide for Power BI)
• Improving DAX performance (Microsoft Documentation: Performance Tuning DAX )
• Configure Direct Lake, including default fallback and refresh behavior
• Implementing incremental refresh for semantic models (Microsoft Documentation: Incremental refresh and real-time data for semantic models)

Microsoft DP-600 Exam FAQs

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Microsoft Certification Exam Policies

Microsoft enforces a consistent and transparent set of certification exam policies to uphold exam integrity, ensure fairness, and provide a uniform testing experience for all candidates. These guidelines apply universally, whether the exam is taken online with remote proctoring or in person at an authorized testing center.

– Exam Retake Policy

Candidates who do not pass an exam on their first attempt must wait a minimum of 24 hours before retaking it. For subsequent attempts, a mandatory 14-day waiting period is required between each attempt. Microsoft permits up to five exam attempts within a 12-month period. Once a candidate passes the exam, no further attempts are allowed unless recertification is required due to exam expiration. Please note that standard fees apply to each attempt, including retakes.

– Rescheduling and Cancellation Policy

Exam appointments may be rescheduled or canceled free of charge if the request is made at least six business days prior to the scheduled exam date. Requests made within five business days of the appointment may incur a rescheduling or cancellation fee. If a candidate cancels within 24 hours of the exam or fails to appear, the full exam fee will be forfeited.

Microsoft DP-600 Exam Study Guide

Step 1: Understand the Exam Objectives

Begin your preparation by thoroughly reviewing the official exam objectives provided by Microsoft. These objectives outline the key skills measured in the exam, including designing and managing semantic models, preparing data for analysis, implementing analytics solutions, and securing data assets. Understanding these domains helps you focus your study efforts on the areas that matter most and ensures you align your learning with the expected outcomes of the certification.

Step 2: Use Official Microsoft Training Resources

Microsoft offers a range of authoritative training materials tailored specifically for the DP-600 exam. Begin with the Microsoft Learn platform, which offers structured learning paths and interactive modules that thoroughly cover each objective. These resources are regularly updated and offer hands-on labs, scenario-based exercises, and real-world examples that reinforce theoretical knowledge. Additionally, consider enrolling in instructor-led courses or Microsoft-certified partner programs for guided learning. However, the training modules covered for this exam include:

• Getting started with Microsoft Fabric
• Implementing a data warehouse with Microsoft Fabric
• Working with semantic models in Microsoft Fabric
• Administering and governing Microsoft Fabric

Step 3: Join Study Groups and Online Communities

Connecting with other candidates and professionals preparing for the same certification can significantly enhance your understanding. Join online forums, LinkedIn groups, or communities such as the Microsoft Tech Community or Reddit’s certification boards. Participating in discussions, sharing insights, asking questions, and accessing shared resources can provide new perspectives and clarify difficult topics. Study groups also help keep you accountable and motivated throughout your preparation journey.

Step 4: Take DP-600 Practice Tests

Practice tests play a crucial role in exam readiness. They help you assess your knowledge, identify gaps, and become familiar with the exam format and question types. Choose reputable platforms that offer up-to-date and exam-relevant practice questions. After completing each mock test, carefully review your answers—especially the incorrect ones—to understand the reasoning and improve your weak areas. Simulating exam conditions also helps you manage time effectively and build confidence for the actual test day.

Step 5: Review and Reinforce Your Knowledge

In the final stages of your preparation, revisit key topics using summaries, flashcards, and revision notes. Focus on areas where you’ve consistently struggled in practice tests or found concepts challenging during study sessions. Consolidate your understanding of KQL, SQL, DAX, semantic modeling techniques, and Microsoft Fabric components. Maintaining a consistent review schedule will help reinforce your knowledge and ensure you’re well-prepared to meet the exam’s technical and analytical demands.