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How to become a Certified Fabric Analytics Engineer – Passing the DP-600 Exam

Microsoft Fabric was introduced almost a year ago and the certification associated with it was released during Jan 2024. It was in beta for 2 months and now it is generally available to be taken up.

Now that the exam is available generally, let us see what you need to prepare and the things you must learn.

DP-600 is a comprehensive exam that has a wide range of topics that needs to be covered before taking the exam.

If this is the first time you are taking a Microsoft Exam, it is good to know the format of the exam to begin with.

Format of the exam

The exam usually has 55-60 questions in the following format:

  • Multiple choice questions with usually 4 choices. The choices are usually provided:
    • Using Radio buttons
    • Using drop downs
    • Using drag and drop
  • Drag and drop the options in the right order
  • True/False questions

The number of questions is around 55-60 and it includes case study questions as well. The case studies are usually 1-2 in number, and they have multiple questions in relation to the case study.

Microsoft documentation will be available for use throughout the exam. But it is important that you balance the allotted time while using the documentation.

Exam Content

The complete skillset required for the exam is described here:

Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric | Microsoft Learn

We have described below the skillsets required from a developer perspective. This makes it easier to understand what are the things that a developer must know.

Interaction Languages

DP-600 requires the candidates be familiar with SQL, Power Query and DAX at least at intermediate level. Python is also necessary, but this has more to do with PySpark functions.

With respect to the skillset, we have provided below the skillset that is asked in a simpler manner.

The content of the exam can be split into categories: Power BI, Fabric Data Engineering and Others

1. Power BI

Within Power BI, we can further subdivide the topics that can be covered


    • Knowing the functions to be used in a formula for deriving the required result
    • Knowing how to analyze the performance of DAX queries
    • Being aware of how to optimize inefficient DAX queries

Semantic Model

    • Having a sound understanding of the semantic models in Fabric
    • Knowing the use cases of Direct Lake and its benefits
    • Knowing the administrative options (tenant settings) associated with Semantic Model
    • Knowledge of using 3rd party tools like Tabular Editor to edit the Semantic Model properties
    • Being aware of TOM C# Scripting

Power BI Desktop

    • Knowing the various GUI functions within Power Query
    • Knowledge of various options in Power Query to explore the data
    • Knowledge of features in Power BI like themes, visuals, and modelling tab
    • Knowledge of using git environment and Deployment pipelines

2. Fabric Data Engineering

Ingestion and Transformation

    • Knowing how to orchestrate with activities
    • Knowing how to use Copy Data
    • Knowing how to use Dataflow Gen 2 for ingestion and transformation capabilities using Power Query


    • Sound Knowledge of working with Delta tables
    • Knowing the various options provided with Spark for working with Delta tables
    • A good understanding of the maintenance operations on Delta tables

One Lake

    • A sound understanding of using shortcuts and its nuances
    • Knowing how to mix Item Permissions and Workspace permissions for granular access
    • Understanding the differences between Lakehouse and Warehouse


    • Knowing the various T-SQL functions that are generally used
    • Having knowledge of T-SQL syntax for statements generally executed in a warehouse


    • Knowing the license options available and identifying the suitable license for specific scenarios
    • Knowledge of tenant settings that are available for Fabric and Power BI
    • Knowledge of Workspace settings and grouping

References for the exam  

Below are the links you can use to start learning from scratch the skillsets required for the exam. Further below, we have provided the links that are helpful for each section of the skillset described in study guide.

Video links that help you to know end to end:

Microsoft Fabric – DP 600 Certificate Training (youtube.com)

Learn Together: How to pass Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric (youtube.com)

Learn Live | Microsoft Learn


Microsoft Fabric documentation – Microsoft Fabric | Microsoft Learn

Get started with Microsoft Fabric – Training | Microsoft Learn

End-to-end tutorials in Microsoft Fabric – Microsoft Fabric | Microsoft Learn

Plan, implement, and manage a solution for data analytics (10–15%)

1. Plan a data analytics environment

TopicResource 1Resource 2
Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units(SKUs)MSDoc
Recommend settings in the Fabric admin portalMSDoc
Choose a data gateway typeMSDoc
Create a custom Power BI report themeMSDoc


2. Implement and Manage a Data Analytics environment

TopicResource 1Resource 2Resource 3
Implement workspace and item-level access controls for Fabric itemsMSDocMSDoc
Implement data sharing for workspaces, warehouses, and lakehousesMSDoc
Manage sensitivity labels in semantic models and lakehousesMSDoc
Configure Fabric-enabled workspace settingsMSDocYoutubeMSDoc
Manage Fabric capacityBlogBlog


3. Manage the Analytics Development Lifecycle

TopicResource 1Resource 2
Implement version control for a workspaceYoutubeMSDoc
Create and manage a Power BI Desktop project(.pbip)MSDoc

Plan and implement deployment solutionsMSDocYoutube
Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic modelsMSDoc
Deploy and manage semantic models by using the XMLA endpointMSDoc
Create and update reusable assets, including Power BI template(.pbit) files, Power BI data source(.pbids) files, and shared semantic modelsBlog


Prepare and serve data (40–45%)

1. Create objects in a lakehouse or warehouse

TopicResource 1Resource 2Resource 3Resource 4
Ingest data by using a data pipeline, dataflow, or notebookBlogYoutubeMSDocMSDoc
Create and manage shortcutsBlogMSDoc
Implement file partitioning for analytics workloads in a lakehouseMSDoc
Create views, functions, and stored proceduresBlogBlog
Enrich data by adding new columns or tablesBlogBlog


2. Copy Data

TopicResource 1Resource 2Resource 3
Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouseMSDocMSDoc
Copy data by using a data pipeline, dataflow, or notebookMSDoc
Add stored procedures, notebooks, and dataflows to a data pipelineMSDocBlog
Schedule data pipelinesMSDoc
Schedule dataflows and notebooksMSDocMSDocYoutube


3. Transform Data

Note: Some of the transformation skills are not specific to any tool and can be achieved using Spark, Dataflow or T-SQL. Links have been provided for each of them.

TopicResource 1Resource 2Resource 3
Implement a data cleansing process
Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensionsMSDocMSDoc
Implement bridge tables for a lakehouse or a warehouseMSDoc
Denormalize dataBlogBlog
Aggregate or de-aggregate dataMSDocBlogBlog

Merge or join dataBlog

Identify and resolve duplicate data, missing data, or null valuesBlogBlog
Convert data types by using SQL or PySparkMSDocBlog
Filter dataBlogMSDoc


4. Optimize Performance

TopicResource 1Resource 2
Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queriesMSDoc
Implement performance improvements in dataflows, notebooks, and SQL queriesBlog
Identify and resolve issues with Delta table file sizesBlogMSDoc


Implement and manage semantic models (20–25%)

1. Design and build semantic models

TopicResource 1Resource 2Resource 3
Choose a storage mode, including Direct LakeBlogBlog
Identify use cases for DAX Studio and Tabular Editor 2YoutubeBlog
Implement a star schema for a semantic modelMSDocBlogBlog
Implement relationships, such as bridge tables and many-to-many relationshipsMSDocBlog
Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functionsMSDocMSDoc
Implement calculation groups, dynamic strings, and field parametersBlog


Design and build large format datasetBlogMSDoc
Design and build composite models that include aggregationsBlogMSDoc
Implement dynamic row-level security and object-level securityBlogMSDoc
Validate row-level security and object-level securityMSDocBlog


2. Optimize enterprise-scale semantic models

TopicResource 1Resource 2Resource 3
Implement performance improvements in queries and report visualsBlogBlogMSDoc
Improve DAX performance by using DAX StudioBlog
Optimize a semantic model by using Tabular Editor 2BlogBlog
Implement incremental refreshBlogMSDoc


Explore and Analyze data (20–25%)

1. Perform exploratory analytics

Note: Within this sub-topic, there exists these skillsets- implementing descriptive, diagnostic, prescriptive and predictive analytics. These skillsets are so vast that they cannot be covered using couple of links. We would suggest working with the various visuals available in Power BI and get an understanding of the different options available.

Design Power BI reports – Training.|Microsoft Learn

Perform advanced analytics in Power BI – Training.|.Microsoft Learn

TopicResource 1Resource 2
Profile dataMSDocYoutube


2. Query Data by using SQL

TopicResource 1Resource 2
Query a lakehouse in Fabric by using SQL queries or the visual query editorMSDocBlog
Query a warehouse in Fabric by using SQL queries or the visual query editorMSDocMSDoc
Connect to and query datasets by using the XMLA endpointBlogBlog

We hope that this blog with the link aggregations will help you in preparing for the DP-600 exam and to become a certified Fabric Analytics Engineer. We wish you all the best from Data Crafters!

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