Microsoft DP-100 Valid Study Notes - DP-100 Latest Braindumps Files

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Microsoft DP-100 Exam Syllabus Topics:

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

>> Microsoft DP-100 Valid Study Notes <<

Microsoft DP-100 Latest Braindumps Files, DP-100 Prepaway Dumps

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q131-Q136):

NEW QUESTION # 131
You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?

Answer: A


NEW QUESTION # 132
You need to build a feature extraction strategy for the local models.
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:

Explanation


NEW QUESTION # 133
You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features with few distinct values.
You need to control whether automated machine learning automatically imputes missing values and encode categorical features as part of the training task. Which enemy of the autumn package should you use?

Answer: A


NEW QUESTION # 134
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?

Answer: C


NEW QUESTION # 135
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:

You need to evaluate the C-Support Vector classification code.
Which evaluation statement 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: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html


NEW QUESTION # 136
......

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