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Exam Information
Official specifications published by Microsoft
Exam Format
100 min
40-60
700
Associate
Registration
$165 USD
Pearson VUE or online proctoring
English, Japanese, Chinese (Simplified), German +6 more
Validity
1 year
Pass renewal assessment on Microsoft Learn (free); Pass a higher-level certification exam; Earn continuing education credits
DP-700 Exam Topics and Domains
DP-700 is organized into 3 weighted domains. Expect to work with Eventstreams, Data warehouse, Error handling, Eventhouse, and more.
1
Implement and manage an analytics solution
32.5%
Configure Microsoft Fabric workspace settings
Configure Spark workspace settingsConfigure domain workspace settingsConfigure OneLake workspace settingsConfigure data workflow workspace settings
- Configure and optimize Spark workspace settings for different workloads
- Implement domain-based workspace organization
- Configure OneLake for optimal performance and cost
- Design effective data workflow configurations
Implement lifecycle management in Fabric
Configure version controlImplement database projectsCreate and configure deployment pipelines
- Implement version control for Fabric items
- Manage database projects and schema evolution
- Deploy items across workspaces using deployment pipelines
Configure security and governance
Implement workspace-level access controlsImplement item-level access controlsImplement row-level, column-level, object-level, and folder/file-level access controlsImplement dynamic data maskingApply sensitivity labels and endorse itemsImplement and use workspace logging
- Implement comprehensive security at multiple levels
- Configure row-level and column-level security
- Apply data governance and compliance controls
- Monitor and audit workspace activities
Orchestrate processes
Choose between a pipeline and a notebookDesign and implement schedules and event-based triggersImplement orchestration patterns with notebooks and pipelines
- Design appropriate orchestration solutions
- Implement complex scheduling and triggering patterns
- Use parameters and dynamic expressions effectively
2
Ingest and transform data
32.5%
Design and implement loading patterns
Design and implement full and incremental data loadsPrepare data for loading into a dimensional modelDesign and implement a loading pattern for streaming data
- Design efficient data loading patterns
- Implement dimensional modeling best practices
- Handle streaming data ingestion effectively
Ingest and transform batch data
Choose an appropriate data storeChoose between dataflows, notebooks, KQL, and T-SQL for data transformationCreate and manage shortcuts to dataImplement mirroringTransform data using PySpark, SQL, and KQLHandle duplicate, missing, and late-arriving data
- Select appropriate data stores for different scenarios
- Choose optimal transformation tools and methods
- Implement data quality and cleansing patterns
Ingest and transform streaming data
Choose an appropriate streaming engineChoose between native storage, mirrored storage, or shortcuts in Real-Time IntelligenceProcess data by using eventstreamsProcess data by using Spark structured streamingProcess data by using KQLCreate windowing functions
- Select appropriate streaming engines and storage
- Implement stream processing patterns
- Design effective windowing strategies
3
Monitor and optimize an analytics solution
35%
Monitor Fabric items
Monitor data ingestionMonitor data transformationMonitor semantic model refreshConfigure alerts
- Monitor all aspects of data ingestion and transformation
- Track semantic model refresh performance
- Design effective alerting strategies
Identify and resolve errors
Identify and resolve pipeline errorsIdentify and resolve dataflow errorsIdentify and resolve notebook errorsIdentify and resolve eventhouse and eventstream errorsIdentify and resolve T-SQL errors
- Diagnose and resolve various types of errors
- Implement effective debugging strategies
- Use appropriate tools for error analysis
Optimize performance
Optimize a lakehouse tableOptimize a pipelineOptimize a data warehouseOptimize eventstreams and eventhousesOptimize Spark performanceOptimize query performance
- Optimize various Fabric components for performance
- Implement best practices for each optimization scenario
- Use performance monitoring to guide optimization
How do I earn this certification?
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for DP-700 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-04-21
- Announcement date: 2024-11-15
Updates
- Microsoft Fabric Capacity F2 SKU New entry-level capacity tier affects cost optimization questions • Release date: 2025-05-01
- Delta Lake 3.0 Enhanced features for MERGE operations and performance • Release date: 2025-04-15
- Deployment Pipelines 2.0 New deployment strategies and rollback capabilities • Release date: 2025-03-01
- KQL Database Enhanced More emphasis on KQL in exam questions • Release date: 2025-02-15
Who should take this exam?
- Experience with data extraction, transformation, and loading
- Proficiency in SQL, PySpark, or KQL
- Understanding of data architectures and patterns
- Familiarity with cloud computing concepts
- Basic knowledge of version control systems