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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explore data and run experiments20-25%- Explore and visualize data
  • 1. Detect anomalies and outliers
  • 2. Profile and validate data
  • 3. Identify features and relationships
- Run experiments
  • 1. Define parameters and configurations
  • 2. Configure experiment runs
  • 3. Track runs with MLflow
  • 4. Use automated machine learning
- Implement pipelines
  • 1. Build reusable components
  • 2. Create and publish pipelines
  • 3. Pass data between steps
  • 4. Schedule and monitor pipelines
Topic 2: Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Apply prompt engineering
  • 3. Use Azure AI Foundry
- Optimize with Retrieval Augmented Generation
  • 1. Prepare and process data
  • 2. Configure Azure AI Search
  • 3. Create vector stores and indexes
- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Test and evaluate responses
  • 3. Optimize for accuracy and safety
Topic 3: Train and deploy models25-30%- Deploy models
  • 1. Deploy to online endpoints
  • 2. Secure endpoints and manage access
  • 3. Configure compute and scaling
  • 4. Deploy to batch endpoints
- Manage models
  • 1. Package and validate models
  • 2. Register and version models
  • 3. Interpret models and explain predictions
- Train models
  • 1. Apply responsible AI principles
  • 2. Run training scripts
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Configure jobs and environments
- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Monitor performance and data drift
  • 3. Update and retrain models
Topic 4: Design and prepare a machine learning solution20-25%- Manage compute resources
  • 1. Create and configure compute targets
  • 2. Select environments
  • 3. Attach and monitor compute
- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 3. Register and manage datastores
- Manage Azure Machine Learning workspace
  • 1. Create and configure workspace
  • 2. Work with registries
  • 3. Set up Git integration
  • 4. Use developer tools and CLI
- Design a machine learning solution
  • 1. Select development approach
  • 2. Plan model deployment requirements
  • 3. Determine dataset structure and format
  • 4. Define compute specifications for workloads

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q144-Q149):

NEW QUESTION # 144
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 145
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi- image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.
You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
An authenticated connection must not be required for testing.
The deployed model must perform with low latency during inferencing.
The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: ds-workstation notebook VM
An authenticated connection must not be required for testing.
On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.
Box 2: gpu-compute cluster
Image classification is well suited for GPU compute clusters
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning


NEW QUESTION # 146
You are using the Azure Machine Learning designer to transform a dataset by using an Execute Python Script component and custom code.
You need to define the method signature for the Execute Python Script component and return value type.
What should you define? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 147
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml


NEW QUESTION # 148
You create a new Azure Machine Learning workspace with a compute cluster.
You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:


NEW QUESTION # 149
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