Use this quick start guide to collect all the information about Implementing Analytics Solutions Using Microsoft Fabric (DP-600) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-600 Implementing Analytics Solutions Using Microsoft Fabric exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCA Fabric Analytics Engineer certification exam.
The Implementing Analytics Solutions Using Microsoft Fabric certification is mainly targeted to those candidates who want to build their career in Microsoft Fabric domain. The Microsoft Certified - Fabric Analytics Engineer Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCA Fabric Analytics Engineer.
Implementing Analytics Solutions Using Microsoft Fabric Exam Summary:
| Exam Name | Microsoft Certified - Fabric Analytics Engineer Associate |
| Exam Code | DP-600 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-600T00-A: Microsoft Fabric Analytics Engineer |
| Schedule Exam | Pearson VUE |
| 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%) |
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Plan a data analytics environment
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- 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
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- 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
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- 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%) |
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Create objects in a lakehouse or warehouse
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- 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
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- 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
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- 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%) |
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Design and build semantic models
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- 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
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- 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%) |
|
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Perform exploratory analytics
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- Implement descriptive and diagnostic analytics
- Integrate prescriptive and predictive analytics into a visual or report - Profile data |
|
Query data by using SQL
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- 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 |
To ensure success in Microsoft MCA Fabric Analytics Engineer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam.