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Microsoft DP-100 Exam Syllabus Topics:

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

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Free PDF Quiz The Best Microsoft - DP-100 - Latest Designing and Implementing a Data Science Solution on Azure Test Practice

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q518-Q523):

NEW QUESTION # 518
You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and mode! artifacts automatically when training a model.
You start by writing the following code segment:

For each of the following statements, select Yes If the statement is true. Otherwise, select No.

Answer:

Explanation:


NEW QUESTION # 519

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:


NEW QUESTION # 520
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:

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


NEW QUESTION # 521
You plan to implement an Azure Machine Learning solution. You have the following requirements:
* Run a Jupyter notebook to interactively tram a machine learning model.
* Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.
You need to select a development technique for each requirement
Which development technique should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation


NEW QUESTION # 522
You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
References:
https://www.edureka.co/blog/classification-algorithms/


NEW QUESTION # 523
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