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Last Updated: Sep 13, 2026

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Microsoft DP-100 Practice Q&A's

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Skills Covered

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

  • 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.

  • 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.

  • 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.

  • 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.

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

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Career Opportunities, Roles, and Salary

After obtaining the Microsoft Certified: Azure Data Scientist Associate certification, you become eligible for the data scientist role. Also, with this certification, you can expect to earn a decent remuneration of about $97k on average annually, as stated by PayScale.com. The expected career paths for a data scientist also include senior data scientist with a salary potential of $126,778 and data science manager with a salary potential of $136,781. Another possible path is that of a data science director with a potential pay of $157,095 annually. However, remember that besides, such a certificate work experience also matters.

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and retrain models10-15%- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows
- Monitor deployed models
  • 1. Monitor model performance
  • 2. Track data drift
Explore data and train models35-40%- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Run experiments and train models
  • 1. Perform hyperparameter tuning
  • 2. Track experiments
  • 3. Use automated machine learning
- Prepare data for modeling
  • 1. Ingest and transform data
  • 2. Manage datasets and datastores
Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks
- Design an Azure Machine Learning workspace
  • 1. Manage compute resources
  • 2. Configure workspace resources
  • 3. Configure security and access
Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy real-time inference endpoints
  • 2. Deploy batch inference pipelines
- Manage deployment assets
  • 1. Create inference configurations
  • 2. Register models
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