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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q282-Q287):

NEW QUESTION # 282
You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?

Answer: B


NEW QUESTION # 283
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine learning Studio to perform feature engineering on a dataset. You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDI) binning mode.
Does the solution meet the goal?

Answer: B


NEW QUESTION # 284
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.
Model performance has degraded due to model input data changes.
You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.
Which event should you use?

Answer: D


NEW QUESTION # 285
You develop a chat flow in an Azure Al Foundry project
You plan to include a Jinja language-based prompt template in the How
You need to complete the provided template to display a list of inputs and outputs included in the flow.
How should you complete the provided template? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 286
You load data from a notebook in an Azure Machine Learning workspace into a panda's cat frame. The data contains 10.000 records. Each record consists of 10 columns.
You must identify the number of missing values in each of the columns.
You need to complete the Python code that will return the number of missing values in each of the columns.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE; Each correct selection it worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 287
......

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