Problem
In March 2024, Microsoft announced the general availability of Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric, which leads to the Microsoft Certified: Fabric Analytics Engineer Associate certification.
As DP-600 is a new Microsoft Fabric certification exam, what is the essential information you need to know about this test? Where can you find the training resources to prepare for and pass this exam? What are the key subject areas this exam covers? Are there any practice assessments you can take to have an overview of the style, wording, and difficulty of the questions you’re likely to experience on the exam?
Solution
In this tip, we will provide the essential information on this exam and the appropriate study materials, including web links, books, online courses, and practice assessments to prepare for and pass the exam.
Who is the Audience of the DP-600 Exam?
As a candidate for this exam, you should have in-depth work experience with the Fabric platform, including subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
By passing this exam, you become a Microsoft Certified: Fabric Analytics Engineer Associate. In this role, your responsibilities include transforming data into reusable analytics assets by using Microsoft Fabric components, such as Lakehouses, Data warehouses, Notebooks, Dataflows, Data pipelines, Semantic models, and Reports; implementing analytics best practices in Fabric, including version control and deployment; as well as having experience with Data modeling, Git-based source control, and languages such as SQL, DAX, and PySpark.
What is the Format, Duration, and Number of Questions of the Exam?
- Duration: 100 minutes.
- Format: Multiple-choice and multiple-response questions with case studies.
- Number of Questions: 40-60.
What is the Needed Score to Pass?
- 700/1000
What Certification Do You Get After Passing the Exam?
- Microsoft Certified: Fabric Analytics Engineer Associate
Which Books Would You Recommend for this Exam?
Exam Ref DP-600 Implementing Analytics Solutions Using Microsoft Fabric by Daniil Maslyuk, Johnny Winter, Stěpán Resl.
- This Exam Ref is the official study guide for the new Microsoft Exam DP-600. This title has not yet been released as of this writing, but you can preorder the book at Amazon.com.
Learn Microsoft Fabric: A practical guide to performing data analytics in the era of artificial intelligence by Arshad Ali, Bradley Schacht.
- This book is a comprehensive introduction to Microsoft Fabric, its components, and the wider analytics landscape. By the end of this book, you’ll have gained a thorough understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric, which is essential to prepare for and pass the exam.
Do You Recommend Practice Tests for this Exam?
Microsoft provides online practice assessments with 50 questions. You can take the test for an overview of the style, wording, and difficulty of the questions you’re likely to experience on the exam.
Are There Courses for this Exam?
Microsoft provides a self-paced, free online course called Course DP-600T00-A: Microsoft Fabric Analytics Engineer. This course is best suited for those who have the PL-300 certification or similar expertise using Power BI. The course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students 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. The course has the following 17 modules:
- Introduction to end-to-end analytics using Microsoft Fabric
- Administer Microsoft Fabric
- Ingest Data with Dataflows Gen2 in Microsoft Fabric
- Ingest data with Spark and Microsoft Fabric notebooks
- Use Data Factory pipelines in Microsoft Fabric
- Get started with lakehouses in Microsoft Fabric
- Organize a Fabric lakehouse using medallion architecture designer
- Use Apache Spark in Microsoft Fabric
- Work with Delta Lake tables in Microsoft Fabric
- Get started with data warehouses in Microsoft Fabric
- Load data into a Microsoft Fabric data warehouse
- Query a data warehouse in Microsoft Fabric
- Monitor a Microsoft Fabric data warehouse
- Understand scalability in Power BI
- Create Power BI model relationships
- Use tools to optimize Power BI performance
- Enforce Power BI model security
Udemy has several courses covering the DP-600 certification exam; among them is one by Phillip Burton that has the most detailed, in-depth, and frequently updated content.
Can You Provide Links to Study for the Exam?
Here are the best study material links we recommend, organized using the Microsoft’s official Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric.
Plan, Implement, and Manage a Solution for Data Analytics (10–15%)
Plan a data analytics environment
- Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
- Recommend settings in the Fabric admin portal
- Choose a data gateway type
- Create a custom Power BI report theme
Implement and Manage a Data Analytics Environment
- Implement workspace and item-level access controls for Fabric items
- Implement data sharing for workspaces, warehouses, and lakehouses
- Manage sensitivity labels in semantic models and lakehouses
- Configure Fabric-enabled workspace settings
- Manage Fabric capacity
Manage the Analytics Development Lifecycle
- Implement version control for a workspace
- Create and manage a Power BI Desktop project (.pbip)
- Plan and implement deployment solutions
- Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
- Deploy and manage semantic models by using the XMLA endpoint
- Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
Prepare and Serve Data (40–45%)
Create Objects in a Lakehouse or Warehouse
- Ingest data by using a data pipeline, dataflow, or notebook
- Create and manage shortcuts
- Implement file partitioning for analytics workloads in a lakehouse
- Create views, functions, and stored procedures
- Enrich data by adding new columns or tables
Copy Data
- Copy data by using a data pipeline, dataflow, or notebook
- Add stored procedures, notebooks, and dataflows to a data pipeline
- Schedule data pipelines
- Schedule dataflows and notebooks
Transform Data
- Implement a data cleansing process
- Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
- Implement bridge tables for a lakehouse or a warehouse
- Denormalize data
- Aggregate or de-aggregate data
- Merge or join data
- Identify and resolve duplicate data, missing data, or null values
- Convert data types by using SQL or PySpark
- Filter data
Optimize Performance
- Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
- Implement performance improvements in dataflows, notebooks, and SQL queries
- Identify and resolve issues with Delta table file sizes
Implement and Manage Semantic Models (20–25%)
Design and Build Semantic Models
- Choose a storage mode, including Direct Lake
- Identify use cases for DAX Studio and Tabular Editor 2
- Implement a star schema for a semantic model
- Implement relationships, such as bridge tables and many-to-many relationships
- Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
- Implement calculation groups, dynamic strings, and field parameters
- Design and build a large format dataset
- Design and build composite models that include aggregations
- Implement dynamic row-level security and object-level security
- Validate row-level security and object-level security
Optimize Enterprise-scale Semantic Models
- Implement performance improvements in queries and report visuals
- Improve DAX performance by using DAX Studio
- Optimize a semantic model by using Tabular Editor 2
- Implement incremental refresh
Explore and Analyze Data (20–25%)
Perform Exploratory Analytics
- Implement descriptive and diagnostic analytics
- Integrate prescriptive and predictive analytics into a visual or report
- Profile data
Query Data by Using SQL
- Query a lakehouse in Fabric by using SQL queries or the visual query editor
- Query a warehouse in Fabric by using SQL queries or the visual query editor
- Connect to and query datasets by using the XMLA endpoint
Next Steps
For more information and exam preparation tips, refer to the links below:
