Practice Exams | MS Azure DP-600 Fabric Analytics Engineer

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

As a candidate for this exam, you should have subject matter expertise in designing, creating, and managing analytical assets, such as semantic models, data warehouses, or lakehouses.

Your responsibilities for this role include:

• Prepare and enrich data for analysis

• Secure and maintain analytics assets

• Implement and manage semantic models

You work closely with stakeholders for business requirements and partner with architects, analysts, engineers, and administrators.

You should also be able to query and analyze data by using Structured Query Language (SQL), Kusto Query Language (KQL), and Data Analysis Expressions (DAX).

Skills at a glance

• Maintain a data analytics solution (25–30%)

• Prepare data (45–50%)

• Implement and manage semantic models (25–30%)

Maintain a data analytics solution (25–30%)

Implement security and governance

• Implement workspace-level access controls

• Implement item-level access controls

• Implement row-level, column-level, object-level, and file-level access control

• Apply sensitivity labels to items

• Endorse items

Maintain the analytics development lifecycle

• Configure version control for a workspace

• Create and manage a Power BI Desktop project (.pbip)

• Create and configure deployment pipelines

• 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 data (45–50%)

Get data

• Create a data connection

• Discover data by using OneLake data hub and real-time hub

• Ingest or access data as needed

• Choose between a lakehouse, warehouse, or eventhouse

• Implement OneLake integration for eventhouse and semantic models

Transform data

• Create views, functions, and stored procedures

• Enrich data by adding new columns or tables

• Implement a star schema for a lakehouse or warehouse

• Denormalize data

• Aggregate data

• Merge or join data

• Identify and resolve duplicate data, missing data, or null values

• Convert column data types

• Filter data

Query and analyze data

• Select, filter, and aggregate data by using the Visual Query Editor

• Select, filter, and aggregate data by using SQL

• Select, filter, and aggregate data by using KQL

Implement and manage semantic models (25–30%)

Design and build semantic models

• Choose a storage mode

• 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 format strings, and field parameters

• Identify use cases for and configure large semantic model storage format

• Design and build composite models

Optimize enterprise-scale semantic models

• Implement performance improvements in queries and report visuals

• Improve DAX performance

• Configure Direct Lake, including default fallback and refresh behavior

• Implement incremental refresh for semantic models