DP-600 Fabric Analytics Engineer Skills Measured Breakdown and Resource Links


As of 12th January 2024, the beta exam for the new Microsoft Certified: Fabric Analytics Engineer Associate is now available. This is the link to the certification page and here is the link to the exam details page. I covered the exam in a previous blog post (and video) available here which breaks down the exam topics.

As I previously did with the DP-500 Enterprise Data Analyst certification, I’ve listed each of the skills measured and linked to what I believe to be the relevant topic.

There are the Learn modules which cover aspects of the exam, you can find those in the Two Ways to Prepare: Self Paced and there are all the Fabric related self-paced training available here. Now, the training there is great and covers a lot of Fabric workloads and well worth completing even if you are not looking at doing the exam.

Pragati Jain has listed links to the various Learn modules in a great blog here so please check that out. Data Mozart (AKA Nikola Ilic) has a section dedicated to the DP600 on his blog, so check that out here. Kevin Chant has extensive blogs around DP600, the latest one is here

I will revisit this blog often to update each skills measured relevant links.

Exam Links

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)Learn
Recommend settings in the Fabric admin portalLearn
Choose a data gateway typeLearn
Create a custom Power BI report themeLearn

Implement and manage a data analytics environment

Implement workspace and item-level access controls for Fabric itemsLearn, Learn
Implement data sharing for workspaces, warehouses, and lakehousesLearn
Manage sensitivity labels in semantic models and lakehousesLearn
Configure Fabric-enabled workspace settingsLearn
Manage Fabric capacityLearn

Manage the analytics development lifecycle

Implement version control for a workspaceLearn
Create and manage a Power BI Desktop project (.pbip)Learn
Plan and implement deployment solutionsLearn
Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic modelsLearn
Deploy and manage semantic models by using the XMLA endpointLearn
Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic modelsTemplates, PBIDS, 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 notebookLearn, Learn, Learn
Create and manage shortcutsLearn
Implement file partitioning for analytics workloads in a lakehouseLearn
Create views, functions, and stored proceduresLearn
Enrich data by adding new columns or tables

Copy data

Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
Copy data by using a data pipeline, dataflow, or notebookLearn, Learn
Add stored procedures, notebooks, and dataflows to a data pipelineLearn, Learn
Schedule data pipelinesLearn
Schedule dataflows and notebooksCommunity

Transform data

Implement a data cleansing processLearn
Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensionsLearn, Learn
Implement bridge tables for a lakehouse or a warehouseLearn
Denormalize dataCommunity
Aggregate or de-aggregate data
Merge or join dataLearn
Identify and resolve duplicate data, missing data, or null valuesLearn
Convert data types by using SQL or PySpark
Filter dataLearn

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 sizesLearn, Learn

Implement and manage semantic models (20–25%)

Design and build semantic models

Choose a storage mode, including Direct LakeLearn, Learn
Identify use cases for DAX Studio and Tabular Editor 2Learn, Community,
Implement a star schema for a semantic modelLearn
Implement relationships, such as bridge tables and many-to-many relationshipsLearn
Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functionsLearn, Learn, Learn, Learn, Learn
Implement calculation groups, dynamic strings, and field parametersLearn, Learn, Learn
Design and build a large format datasetLearn
Design and build composite models that include aggregationsLearn, Learn, Community
Implement dynamic row-level security and object-level securityLearn, Learn, Community
Validate row-level security and object-level securityLearn

Optimize enterprise-scale semantic models

Implement performance improvements in queries and report visualsCommunity
Improve DAX performance by using DAX StudioCommunity, Community
Optimize a semantic model by using Tabular Editor 2Learn
Implement incremental refreshLearn

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 dataLearn

Query data by using SQL

Query a lakehouse in Fabric by using SQL queries or the visual query editorLearn
Query a warehouse in Fabric by using SQL queries or the visual query editorLearn, Learn
Connect to and query datasets by using the XMLA endpointLearn


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