Microsoft : DP-100 Questions & Answers

Updated: Sep 10, 2026

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Certification Path

The Microsoft Certified Azure Data Scientist Associate Certification includes only one DP-100 Exam.

How to book the DP-100 Exam

These are following steps for registering the DP-100 exam. Step 1: Visit to Microsoft Exam Registration Step 2: Signup/Login to MICROSOFT account Step 3: Search for MICROSOFT DP-100 Certifications Exam Step 4: Select Date and Center of examination and confirm with payment value of $165

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

Knowing Associated Certification

The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 exam will also help the test-taker to gain some ACE college credits.

The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.
  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.

Microsoft DP-100 Exam Syllabus Topics:

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

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