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Microsoft DP-700 Exam Syllabus

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Before starting your DP-700 exam preparation, it is recommended to review the complete Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free DP-700 questions. We also provide premium DP-700 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Microsoft
Vendor
DP-700
Exam Code
142
Total Questions
3
Total Exam Domains

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DP-700 EXAM QUESTIONS

Microsoft DP-700 Exam Objectives

Section 1: Implement and manage an analytics solution
Weight:
30-35%
Configure Microsoft Fabric workspace settings
  • Configure Spark workspace settings
  • Configure domain workspace settings
  • Configure OneLake workspace settings
  • Configure Apache Airflow 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 Microsoft Fabric audit logs
  • Configure and implement OneLake security
Orchestrate processes
  • Choose between Dataflow gen 2, 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
Section 2: Ingest and transform data
Weight:
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 Gen2, notebooks, KQL, and T-SQL for data transformation
  • Create and manage OneLake shortcuts
  • 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 tables and OneLake shortcuts in Real-Time Intelligence
  • Choose between Query acceleration for OneLake shortcuts and standard OneLake shortcuts in Real-Time Intelligence
  • Process data by using Eventstream
  • Process data by using Spark structured streaming
  • Process data by using KQL
  • Create windowing functions
Section 3: Monitor and optimize an analytics solution
Weight:
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 Gen2 errors
  • Identify and resolve notebook errors
  • Identify and resolve Eventhouse errors
  • Identify and resolve Eventstream errors
  • Identify and resolve T-SQL errors
  • Identify and resolve OneLake shortcut errors
Optimize performance
  • Optimize a Lakehouse table
  • Optimize a pipeline
  • Optimize a data warehouse
  • Optimize Eventstream and Eventhouse
  • Optimize Spark performance
  • Optimize query performance
Info
https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-700/