Step 3: Taking Online Learning Paths
In addition, Microsoft recommends that the candidates for the DP-100 exam take online courses to adequately prepare for their test. You can find the following learning paths on the official certification webpage:
- Create No-Code Predictive Models with Azure Machine Learning
- Build AI Solutions with Azure Machine Learning
- Create Machine Learning Models
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Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam
Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam which is related to Microsoft Certified Azure Data Scientist Associate Certification. This DP-100 exam validates the ability to apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. This DP-100 exam also tests the Candidate knowledge to use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates must have skills to use applications that involve natural language processing, speech, computer vision, and predictive analytics. Azure Data Scientist usually hold or pursue this certification and you can expect the same job role after completion of this certification.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
DP-100 Exam Target Audience
Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.
4. Models Deployment and Consumption (20-25%):
- Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
- Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
- Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
- Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Optimize language models for AI applications | 25-30% | - Evaluate and improve models
- 1. Test and evaluate responses
- 2. Optimize for accuracy and safety
- 3. Apply responsible generative AI
- Optimize with Retrieval Augmented Generation
- 1. Create vector stores and indexes
- 2. Prepare and process data
- 3. Configure Azure AI Search
- Implement generative AI solutions
- 1. Use Azure AI Foundry
- 2. Apply prompt engineering
- 3. Build prompt flows
|
| Topic 2: Train and deploy models | 25-30% | - Train models
- 1. Use HyperDrive for hyperparameter tuning
- 2. Configure jobs and environments
- 3. Run training scripts
- 4. Apply responsible AI principles
- Deploy models
- 1. Deploy to batch endpoints
- 2. Secure endpoints and manage access
- 3. Deploy to online endpoints
- 4. Configure compute and scaling
- Manage models
- 1. Interpret models and explain predictions
- 2. Register and version models
- 3. Package and validate models
- Monitor and maintain models
- 1. Monitor performance and data drift
- 2. Update and retrain models
- 3. Implement MLOps practices
|
| Topic 3: Explore data and run experiments | 20-25% | - Explore and visualize data
- 1. Detect anomalies and outliers
- 2. Profile and validate data
- 3. Identify features and relationships
- Run experiments
- 1. Configure experiment runs
- 2. Use automated machine learning
- 3. Track runs with MLflow
- 4. Define parameters and configurations
- Implement pipelines
- 1. Build reusable components
- 2. Create and publish pipelines
- 3. Schedule and monitor pipelines
- 4. Pass data between steps
|
| Topic 4: Design and prepare a machine learning solution | 20-25% | - Manage data assets
- 1. Select storage services
- 2. Register and manage datastores
- 3. Create and maintain data assets
- Manage Azure Machine Learning workspace
- 1. Work with registries
- 2. Create and configure workspace
- 3. Set up Git integration
- 4. Use developer tools and CLI
- Manage compute resources
- 1. Select environments
- 2. Create and configure compute targets
- 3. Attach and monitor compute
- Design a machine learning solution
- 1. Select development approach
- 2. Plan model deployment requirements
- 3. Define compute specifications for workloads
- 4. Determine dataset structure and format
|