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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q409-Q414):

NEW QUESTION # 409
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: 500
For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.
A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.
Here we must replicate the findings.
Box 2: Mean Absolute Error
Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance


NEW QUESTION # 410
You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:

Does the solution meet the goal?

Answer: A


NEW QUESTION # 411
A coworker registers a datastore in a Machine Learning services workspace by using the following code:

You need to write code to access the datastore from a notebook.

Answer:

Explanation:

Explanation

Box 1: DataStore
To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:
# Get a named datastore from the current workspace
datastore = Datastore.get(ws, datastore_name='your datastore name')
Box 2: ws
Box 3: demo_datastore
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data


NEW QUESTION # 412
You have a dataset that includes confidential dat
a. You use the dataset to train a model.
You must use a differential privacy parameter to keep the data of individuals safe and private.
You need to reduce the effect of user data on aggregated results.
What should you do?

Answer: B

Explanation:
Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy


NEW QUESTION # 413
You create a binary classification model using Azure Machine Learning Studio.
You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
You need to create the required business metrics.
How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 414
......

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