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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q100-Q105):

NEW QUESTION # 100
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself.
Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


NEW QUESTION # 101
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data.
You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Explanation

The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.


NEW QUESTION # 102
You must use in Azure Data Science Virtual Machine (DSVM) as a compute target.
You need to attach an existing DSVM to the workspace by using the Azure Machine Learning SDK for Python.
How should you complete the following code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 103
You create an Azure Machine Learning dataset. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
You must upload the script and associated libraries as a script bundle.
You need to configure the Execute Python Script component.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 104
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible Al (RAI) dashboard for model1.
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:

Explanation:


NEW QUESTION # 105
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