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Skills Covered

To nail DP-100, you will need to scrutinize the below-mentioned areas:

  • Set up Azure ML Workspace

    The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.

  • Execute Experiments & Train Models

    This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.

  • Manage and Optimize Models

    Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.

  • Deploy and Consume Models

    The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Constantly updated system

As you know, today's society is changing very fast. We also need new knowledge to fill in as we learn. DP-100 exam simulation: Designing and Implementing a Data Science Solution on Azure is selected many experts and constantly supplements and adjust our questions and answers. When you use our study materials, you can find the information you need at any time. When we update the DP-100 preparation questions, we will take into account changes in society, and we will also draw user feedback. If you have any thoughts and opinions in using our study materials, you can tell us. We hope to grow with you and the continuous improvement of DP-100 training engine is to give you the best quality experience.

Step 2: Enrolling for the Instructor-Led Training Course

This is the most recommended way of preparing for the Microsoft DP-100 exam. Microsoft offers the official training course under the title “Designing and Implementing a Data Science Solution on Azure” to help the students build a solid knowledge base for the test. The training sessions are delivered to the individuals by the world-class professionals with plenty of experience in the field. So, the candidates get trained accordingly in the best way possible. Another advantage of taking the instructor-led course is that you have an opportunity to ask your questions and receive feedback whenever you face difficulties.

In today's rapid economic development, society has also put forward higher and higher requirements for us. In addition to the necessary theoretical knowledge, we need more skills. Our DP-100 exam simulation: Designing and Implementing a Data Science Solution on Azure is a great tool to improve our competitiveness. After we use our study materials, we can get the Microsoft certification faster. This certification gives us more opportunities. Compared with your colleagues around you, with the help of our DP-100 preparation questions, you will also be able to have more efficient work performance. Our study materials can bring you so many benefits because they have the following features. I hope you can use a cup of coffee to learn about our DP-100 training engine. Perhaps this is the beginning of your change.

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Microsoft DP-100 Exam Syllabus Topics:

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

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