DP 600: Fabric Analytics Engineer Practice Test 2025

DP 600: Fabric Analytics Engineer Practice Test 2025 free download

100 EXAM READY Question Answer + Explanation: Latest assessement / quick case study simulates the actual DP-600 Exam

PRACTICE - PRACTICE - PRACTICE: PRACTICE WILL MAKE YOU PERFECT & become Microsoft Certified: Fabric Analytics Engineer Associate

Course provides several practice sets similar to actual exam questions as per official exam syllabus and study guide for DP-600: Implementing Analytics Solutions Using Microsoft Fabric.

To excel in this exam, you should possess in-depth knowledge of designing, developing, and overseeing analytical assets, including semantic models, data warehouses, and lakehouses.

As you prepare for this certification, you'll gain the expertise needed to solve real-world challenges by mastering key Fabric components.

1. Lakehouse

2. Warehouse

3. Eventhouse / KQL Database

4. Spark Notebook

5. Dataflows

6. Semantic model

7. Report

As a candidate for this certification 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

• Solution architects

• Data architects

• Data analysts,

• Data engineers,

• Data scientists

• AI engineers

• 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).

Practice set contains questions from all 3 below domains and once you attended a practice set, you can review where you will get the actual answers along with EXPLANATION and official/course resource link.

• 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