As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. Your responsibilities for this role include:

Ingesting and transforming data.
Securing and managing an analytics solution.
Monitoring and optimizing an analytics solution.

You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics.

You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).

Schedule exam
Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric (beta)
Languages: English
Retirement date: none

This exam measures your ability to accomplish the following technical tasks: ingesting and transforming data; securing and managing an analytics solution; and monitoring and optimizing an analytics solution.

Skills measured
Implement and manage an analytics solution (30–35%)
Ingest and transform data (30–35%)
Monitor and optimize an analytics solution (30–35%)

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Purpose of this document

This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.

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Exam scoring and score reports A score of 700 or greater is required to pass.
Exam sandbox You can explore the exam environment by visiting our exam sandbox.
Request accommodations If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation.

About the exam
Languages
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. If the exam isn’t available in your preferred language, you can request an additional 30 minutes to complete the exam.

Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.

Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Skills measured
Audience profile
As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. Your responsibilities for this role include:
Ingesting and transforming data.
Securing and managing an analytics solution.
Monitoring and optimizing an analytics solution.
You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics.

You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).
Skills at a glance

Implement and manage an analytics solution (30–35%)
Ingest and transform data (30–35%)
Monitor and optimize an analytics solution (30–35%)

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 file-level access controls
Implement dynamic data masking
Apply sensitivity labels to items
Endorse items
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, 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
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

Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.

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Sample Question and Answers

QUESTION 1
You need to ensure that the data analysts can access the gold layer lakehouse.
What should you do?

A. Add the DataAnalyst group to the Viewer role for WorkspaceA.
B. Share the lakehouse with the DataAnalysts group and grant the Build reports on the default semantic model permission.
C. Share the lakehouse with the DataAnalysts group and grant the Read all SQL Endpoint data permission.
D. Share the lakehouse with the DataAnalysts group and grant the Read all Apache Spark permission.

Answer: C

Explanation:
Data Analysts’ Access Requirements must only have read access to the Delta tables in the gold layer
and not have access to the bronze and silver layers.
The gold layer data is typically queried via SQL Endpoints. Granting the Read all SQL Endpoint data
permission allows data analysts to query the data using familiar SQL-based tools while restricting
access to the underlying files.

QUESTION 2
HOTSPOT
You need to recommend a method to populate the POS1 data to the lakehouse medallion layers.
What should you recommend for each layer? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
Bronze Layer: A pipeline Copy activity
The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this
layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and
the ability to ingest the data directly into the Delta format in the bronze layer.
Silver Layer: A notebook
The silver layer involves extensive data cleansing (deduplication, handling missing values, and
standardizing capitalization). A notebook provides the flexibility to implement complex
transformations and is well-suited for this task.

QUESTION 3
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?

A. Create a workspace identity and enable high concurrency for the notebooks.
B. Create a shortcut and ensure that caching is disabled for the workspace.
C. Create a workspace identity and use the identity in a data pipeline.
D. Create a shortcut and ensure that caching is enabled for the workspace.

Answer: B

Explanation:
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we
must address two key points:
Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric
does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to
the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw
data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly
from the Amazon S3 bucket.

QUESTION 4
HOTSPOT
You need to create the product dimension.
How should you complete the Apache Spark SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Join between Products and ProductSubCategories:
Use an INNER JOIN.
The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that
only matching records (i.e., products with a valid subcategory) are included.
Join between ProductSubCategories and ProductCategories:
Use an INNER JOIN.
Similar to the above logic, we want to include only subcategories assigned to a valid product
category. An INNER JOIN ensures this condition is met.
WHERE Clause
Condition: IsActive = 1
Only active products (where IsActive equals 1) should be included in the gold layer. This filters out inactive products.

QUESTION 5
You need to populate the MAR1 data in the bronze layer.
Which two types of activities should you include in the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. ForEach
B. Copy data
C. WebHook
D. Stored procedure

Answer: AB

Explanation:
MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required
to iterate over these endpoints to fetch data from each one. It enables dynamic execution of API calls
for each entity.
The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the
bronze layer in Delta format. It supports native connectors for REST APIs and Delta, minimizing development effort.