Study Guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric (beta)
Important
This exam will be available on October 22, 2024. Learn more about the upcoming beta exam DP-700.
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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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.
Study resources | Links to learning and documentation |
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Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
Find documentation | Microsoft Fabric What is Data engineering in Microsoft Fabric? |
Ask a question | Microsoft Q&A | Microsoft Docs |
Get community support | Analytics on Azure - Microsoft Tech Community Microsoft Fabric Blog |
Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
Find a video | Exam Readiness Zone Data Exposed Browse other Microsoft Learn shows |