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Exam Information
Official specifications published by Microsoft
Exam Format
120 min
700
Associate
Registration
$165 USD
Pearson VUE or online proctoring
Validity
2 years
Pass renewal assessment on Microsoft Learn (free); Earn a higher-level certification; Complete continuing education requirements
DP-100 Exam Topics and Domains
DP-100 is organized into 4 weighted domains. Expect to work with Azure AI Studio, Azure ML, Azure OpenAI, Azure ML HyperDrive, and more.
1
Design and prepare a machine learning solution
22.5%
Design a machine learning solution
Identify the structure and format for datasetsDetermine the compute specifications for machine learning workloadSelect the development approach to train a model
- Design end-to-end machine learning solutions on Azure
- Select appropriate compute and storage resources
- Choose the right development approach for the scenario
Create and manage resources in an Azure Machine Learning workspace
Create and manage a workspaceCreate and manage datastoresCreate and manage compute targetsSet up Git integration for source control
- Create and configure Azure ML workspaces
- Manage compute and storage resources
- Implement source control for ML projects
Create and manage assets in an Azure Machine Learning workspace
Create and manage data assetsCreate and manage environmentsShare assets across workspaces by using registries
- Manage ML assets lifecycle
- Configure environments for reproducibility
- Implement asset sharing strategies
2
Explore data, and run experiments
22.5%
Use automated machine learning to explore optimal models
Use automated machine learning for tabular dataUse automated machine learning for computer visionUse automated machine learning for natural language processingSelect and understand training optionsEvaluate an automated machine learning run
- Configure and run AutoML experiments
- Evaluate and interpret AutoML results
- Apply AutoML to different data types
Use notebooks for custom model training
Use the terminal to configure a compute instanceAccess and wrangle data in notebooksWrangle data with Synapse Spark poolsRetrieve features from a feature storeTrack model training by using MLflowEvaluate a model, including responsible AI guidelines
- Develop custom models in notebooks
- Track experiments with MLflow
- Evaluate models for performance and fairness
Automate hyperparameter tuning
Select a sampling methodDefine the search spaceDefine the primary metricDefine early termination options
- Configure hyperparameter tuning experiments
- Optimize model performance through tuning
- Implement efficient search strategies
3
Train and deploy models
27.5%
Run model training scripts
Consume data in a jobConfigure compute for a job runConfigure an environment for a job runTrack model training with MLflow in a job runDefine parameters for a jobRun a script as a jobUse logs to troubleshoot job run errors
- Submit and manage training jobs
- Configure job resources and environments
- Troubleshoot job failures
Implement training pipelines
Create custom componentsCreate a pipelinePass data between steps in a pipelineRun and schedule a pipelineMonitor and troubleshoot pipeline runs
- Design and implement ML pipelines
- Orchestrate complex ML workflows
- Schedule and monitor pipeline executions
Manage models
Define the signature in the MLmodel filePackage a feature retrieval specification with the model artifactRegister an MLflow modelAssess a model by using responsible AI principles
- Manage model lifecycle
- Implement model versioning
- Apply responsible AI practices
Deploy a model
Configure settings for online deploymentDeploy a model to an online endpointTest an online deployed serviceConfigure compute for a batch deploymentDeploy a model to a batch endpointInvoke the batch endpoint to start a batch scoring job
- Deploy models for real-time and batch inference
- Configure and manage endpoints
- Test and monitor deployments
4
Optimize language models for AI applications
27.5%
Prepare for model optimization
Select and deploy a language model from the model catalogCompare language models using benchmarksTest a deployed language model in the playgroundSelect an optimization approach
- Select appropriate language models
- Evaluate model performance
- Choose optimization strategies
Optimize through prompt engineering and prompt flow
Test prompts with manual evaluationDefine and track prompt variantsCreate prompt templatesDefine chaining logic with the prompt flow SDKUse tracing to evaluate your flow
- Master prompt engineering techniques
- Build complex prompt flows
- Optimize prompt performance
Optimize through Retrieval Augmented Generation (RAG)
Prepare data for RAGConfigure a vector storeConfigure an Azure AI Search-based index storeEvaluate your RAG solution
- Implement RAG solutions
- Configure vector search systems
- Evaluate RAG effectiveness
Optimize through fine-tuning
Prepare data for fine-tuningSelect an appropriate base modelRun a fine-tuning jobEvaluate your fine-tuned model
- Implement model fine-tuning
- Prepare and validate training data
- Evaluate fine-tuning results
How do I earn this certification?
Passing DP-100 earns the Microsoft Certified: Azure Data Scientist Associate 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-100 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: 2025-04-11
- Announcement date: 2025-01-16
Updates
- Azure OpenAI Service 2024-08-01-preview Critical for language model optimization domain • Release date: 2025-08-01
- Prompt flow 1.10.0 Essential for prompt engineering topics • Release date: 2025-06-15
- Azure AI Search 2024-07-01 Core component for RAG implementations • Release date: 2025-07-01
Who should take this exam?
This exam is typically taken by Data Scientists and Machine Learning Engineers.
- Knowledge of Azure Machine Learning and MLflow
- Experience with Python for data science
- Understanding of machine learning concepts
- Familiarity with Azure services
- Experience with data preprocessing and feature engineering
- Basic knowledge of responsible AI principles