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 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

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