DP-700 Practice Test 2025 (Fabric Data Engineer Associate)
DP-700 Practice Test Qs | Scripts to simulate lab | Concise, demo-driven explanation for right/wrong answer | Case Study
DP-700 Practice Test 2025 (Fabric Data Engineer Associate) free download
DP-700 Practice Test Qs | Scripts to simulate lab | Concise, demo-driven explanation for right/wrong answer | Case Study
<<The course is updated as per the skills measured on April 21, 2025>>
WHY SHOULD YOU BUY MY DP-700 Fabric Data Engineer Associate MOCK TEST?
a. Deeply researched exam questions for DP 700. I create no more than one question/day to maintain high quality.
b. No simple one-liner questions. Each question is based on your understanding of a scenario. The questions challenge you to understand, apply, and analyze your knowledge.
c. This course comes with both clear and lucid video and text explanations. The text explanations come with product illustrations for easy understanding. You can also go through the video explanations for a more seamless demo.
d. For each question I provide a Python Notebook/PowerQuery project file/scripts to simulate the environment used in the question.
e. For each question I provide a summarized version of the answer (suitable for revisions) and a detailed answer (for in-depth learning).
f. I simulate the actual DP700 Fabric Data Engineer exam experience for you in the form of drag-and-drop questions, dropdown questions, multiple yes/no questions with a radio button, repeated scenario questions, etc.
g. No dumping of text in a ppt. PPTs are used only to demo architecture to enhance your understanding.
h. Explanations run parallel to the product. Every detailed explanation has corroborating evidence with the Microsoft product (like Microsoft Fabric) shown in screenshots and clear callouts.
i. Explanations are NOT directly copied from Microsoft documentation. I have rephrased all the reasoning in a simple and easy-to-understand language.
j. No step-motherly treatment of incorrect answer choices. I took enough effort to explain the rationale for each answer choice (whether correct/wrong), including the reference links.
k. Don't worry about inaccurate sentence framing/wrong grammar/incorrect punctuation. I use Grammarly to review every question.
l. Almost non-existent repetition of questions only to increase the question count.
m. I love to help you succeed. If you need to discuss, we have an Active Q&A dashboard and expect fast responses (save for my sleeping hours, which are generally less).
n. As soon as there is an update from Microsoft, I try to update my course, keeping it always fresh.
o. The question bank is peer-reviewed every three months to ensure exam relevance.
p. Case Study: Contoware Analytics Modernization to help you better prepare for the exam.
The questions are collected from a variety of domains and sub-domains with extra care taken to equal attention to each exam area. Also, the questions are on different levels.
For example:
Remember-level questions test whether you can recall memorized facts, & basic concepts.
Understand-level questions validate whether you can explain the meanings of terms, & concepts.
Application-level questions test whether you can perform tasks using facts, concepts, & techniques, and,
Analysis-level questions validate whether you can diagnose situations & solve problems with concepts & techniques.
A mixture of questions at different levels reinforces your knowledge and prepares you to ace the exam.
These are the exam domains covered in the DP-700 practice exam:
Implement and manage an analytics solution (30–35%)
Configure Microsoft Fabric workspace settings
Configure Spark workspace settings
Configure domain workspace settings
Configure OneLake workspace settings
Configure data workflow workspace settings
Implement lifecycle management in Fabric
Configure version control
Implement database projects
Create and configure deployment pipelines
Configure security and governance
Implement workspace-level access controls
Implement item-level access controls
Implement row-level, column-level, object-level, and folder/file-level access controls
Implement dynamic data masking
Apply sensitivity labels to items
Endorse items
Implement and use workspace logging
Orchestrate processes
Choose between a pipeline and a notebook
Design and implement schedules and event-based triggers
Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions
Ingest and transform data (30–35%)
Design and implement loading patterns
Design and implement full and incremental data loads
Prepare data for loading into a dimensional model
Design and implement a loading pattern for streaming data
Ingest and transform batch data
Choose an appropriate data store
Choose between dataflows, notebooks, KQL, and T-SQL for data transformation
Create and manage shortcuts to data
Implement mirroring
Ingest data by using pipelines
Transform data by using PySpark, SQL, and KQL
Denormalize data
Group and aggregate data
Handle duplicate, missing, and late-arriving data
Ingest and transform streaming data
Choose an appropriate streaming engine
Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence
Process data by using eventstreams
Process data by using Spark structured streaming
Process data by using KQL
Create windowing functions
Monitor and optimize an analytics solution (30–35%)
Monitor Fabric items
Monitor data ingestion
Monitor data transformation
Monitor semantic model refresh
Configure alerts
Identify and resolve errors
Identify and resolve pipeline errors
Identify and resolve dataflow errors
Identify and resolve notebook errors
Identify and resolve eventhouse errors
Identify and resolve eventstream errors
Identify and resolve T-SQL errors
Optimize performance
Optimize a lakehouse table
Optimize a pipeline
Optimize a data warehouse
Optimize eventstreams and eventhouses
Optimize Spark performance
Optimize query performance
