Microsoft DP-100 Exam Questions : Designing and Implementing a Data Science Solution on Azure

  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure
  • Updated: Sep 10, 2026
  • Q&As: 528 Questions and Answers

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Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

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3. Models Management and Optimization (20-25%):

  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
  • Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
  • Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.

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

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Microsoft DP-100 Exam Syllabus Topics:

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

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