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

Microsoft DP-100 test insides dumps
  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure
  • Updated: Jul 26, 2026
  • Q & A: 528 Questions and Answers
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3. Models Management and Optimization (20-25%):
  • 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.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • 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.
  • 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.

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

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4. Models Deployment and Consumption (20-25%):
  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
Microsoft DP-100 Exam Syllabus Topics:
SectionWeightObjectives
Prepare a model for deployment20-25%- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
- Deploy machine learning models
  • 1. Deploy real-time inference endpoints
  • 2. Deploy batch inference pipelines
Explore data and train models35-40%- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Run experiments and train models
  • 1. Track experiments
  • 2. Use automated machine learning
  • 3. Perform hyperparameter tuning
Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Manage compute resources
  • 2. Configure workspace resources
  • 3. Configure security and access
- 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. Manage ML pipelines
  • 2. Create scheduled retraining workflows

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