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

  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure
  • Updated: Aug 20, 2026
  • Q & A: 528 Questions and Answers

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

Microsoft DP-100: Skills Measured

Microsoft provides you with the elaborate outline of the skills that you need to acquire before attempting the test. The specific topics of the exam along with the main subtopics are enumerated below:

  • Perform Feature Engineering (15%)

    Answering the questions that are drawn from this domain, the test takers should be able to perform the tasks such as performing feature selection as well as performing feature extraction.

  • Define and Prepare the Development Environment (15%)

    To answer the questions within this objective, the applicants should have the professional ability to accomplish such technical tasks as selecting a development environment; quantifying the business problem; setting up a development environment; etc.

  • Develop Models (40%)

    The skills measured within this topic include selecting an algorithmic approach; evaluating model performance; training the model; identifying data imbalances; splitting datasets, and so on.

  • Prepare Data for Modeling (25%)

    This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.

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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.

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

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Who should take the DP-100 exam

The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled as a Microsoft Certified Azure Data Scientist Associate. If candidates want significant improvement in career growth needs enhanced knowledge, skills, and talents. The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam then he should take this exam.

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Skills measured

  • Implement responsible machine learning (5–10%)
  • Deploy and operationalize machine learning solutions (35–40%)
  • Manage Azure resources for machine learning (25–30%)
  • Run experiments and train models (20–25%)

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 batch inference pipelines
  • 2. Deploy real-time inference endpoints
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. Manage datasets and datastores
  • 2. Ingest and transform data
- 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
Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Configure workspace resources
  • 2. Manage compute resources
  • 3. Configure security and access
- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks

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