As a candidate for this certification, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as:
◉ Lakehouses
◉ Data warehouses
◉ Notebooks
◉ Dataflows
◉ Data pipelines
◉ Semantic models
◉ Reports
You implement analytics best practices in Fabric, including version control and deployment.
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
Implementing Analytics Solutions Using Microsoft Fabric Exam Summary:
| Exam Name | Microsoft Certified - Fabric Analytics Engineer Associate |
| Exam Code | DP-600 |
| Exam Price | $165 (USD) |
| Exam Price | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-600T00-A: Microsoft Fabric Analytics Engineer |
| Sample Questions | Implementing Analytics Solutions Using Microsoft Fabric Sample Questions |
| Practice Exam | Microsoft DP-600 Certification Practice Exam |
Microsoft DP-600 Exam Syllabus Topics:
| Topic | Details |
| 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 | - 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, 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 |
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