DP-600 Certification

The DP-600 exam (Fabric Analytics Engineer) covers a broad range of skills and tools related to Microsoft Fabric. Think of components and concepts such as

• Data Lakehouse
• Data warehouse
• Data modeling
• Data transformation
• Notebooks
• Dataflows Gen2
• Semantic model

To prepare for this exam, you will find the information useful, and that will, in the end, help you master all the necessary skills to become a certified Fabric Analytics Engineer.

To implement solutions as a fabric analytics engineer, you partner with other roles, such as:

• Solution architects
• Data engineers
• Data scientists
• AI engineers
• Database administrators
• Power BI data analysts

In addition to in-depth work with the Fabric platform, you need experience with

• Data modeling
• Data transformation
• Git-based source control
• Exploratory analytics
• Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark

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 lake houses
• Manage sensitivity labels in semantic models and lake houses
• 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 lake houses, 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 lake house 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
• 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 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
• Identify and resolve data loading performance bottlenecks in notebooks
• Identify and resolve data loading performance bottlenecks in 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
• Implement dynamic strings
• Implement field parameters
• Design and build a large format dataset
• Design and build composite models
• Aggregations in Power BI
• Implement and validate dynamic row-level security
• Implement and validate 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

My personal experience and general questions:

How’s the exam? Is it difficult?

• It was quite challenging. The multiple-choice format does make the experience a bit easier since you don’t need to necessarily know the code; you just have to be familiar with it.
• The passing score is 700, and I got 868/1000. The exam is timed for 90 90minutes, and it took me 82 minutes to complete it.

Is Pearson Vue still strict with your exam desk?

• Very much. I needed to remove the following items from my desk: extra (work) laptop, mobile phone, smartwatch, papers and magazines, and wallets, and keep a clear vision of the desk and all surroundings.
• I do want to say this: the proctor was very courteous and patient as I fumbled around in moving my stuff. Kudos to Pearson Vue.

How did I prepare for the exam?

• I worked on MS Learn fabric, practice exercises, and answer MS practice assessment questions
• Learning Path:
• https://learn.microsoft.com/en-us/training/paths/get-started-fabric/
• Practice assessment:
• https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-600/practice/assessment?assessment-type=practice&assessmentId=90

Would I recommend this certification

• Since I came from PL-300, I strongly recommend the DP-600. Data Engineering (and everything Fabric) felt like a natural evolution of the PL-300 base. My fundamentals in DAX and Power Query definitely helped me with DP-600.

Feel free to ask questions in the comments.

All the best!!