Microsoft DP-600 Fabric Analytic Engineer Practice Exam Test

Description

These practice tests closely resemble real exam questions you may encounter in the DP-600 exam. They cover all areas of the syllabus and test your knowledge thoroughly. Since many answer options may seem correct, I’ve provided brief explanations for why certain options are incorrect. If you diligently work through these tests and stay dedicated to learning the Microsoft Fabric concepts, I guarantee you’ll be prepared to pass the exam with confidence.

Below are the skills that will be tested by Practice Tests.

Skills at a glance

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

• Prepare and serve data (40–45%)

• Implement and manage semantic models (20–25%)

• Explore and analyze data (20–25%)

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

Additional resources

Training

Module

Explore fundamentals of large-scale data analytics - Training

Organizations use analytics platforms to build large scale data analytics solutions that generate insights and drive success. Microsoft provides multiple technologies that you can combine to build a large scale data analytics solution.

Certification

Who this course is for:

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