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

DP-100

Exam Code: DP-100

Exam Name: Designing and Implementing a Data Science Solution on Azure

Updated: Aug 17, 2026

Q&A Number: 528 Q&As

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About Microsoft DP-100 Exam Questions and Answers

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Obligatory Prerequisites

Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.

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

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Certification Path

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

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Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam

Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam which is related to Microsoft Certified Azure Data Scientist Associate Certification. This DP-100 exam validates the ability to apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. This DP-100 exam also tests the Candidate knowledge to use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates must have skills to use applications that involve natural language processing, speech, computer vision, and predictive analytics. Azure Data Scientist usually hold or pursue this certification and you can expect the same job role after completion of this certification.

Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Configure security and access
  • 2. Configure workspace resources
  • 3. Manage compute resources
- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks
Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
- Manage deployment assets
  • 1. Create inference configurations
  • 2. Register models
Explore data and train models35-40%- Prepare data for modeling
  • 1. Ingest and transform data
  • 2. Manage datasets and datastores
- Run experiments and train models
  • 1. Use automated machine learning
  • 2. Perform hyperparameter tuning
  • 3. Track experiments
- Optimize model performance
  • 1. Evaluate models
  • 2. Improve accuracy and performance
Deploy and retrain models10-15%- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines
- Monitor deployed models
  • 1. Monitor model performance
  • 2. Track data drift
DP-100 Related Exams
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AZ-900-JPN - Microsoft Azure Fundamentals (AZ-900日本語版)
AZ-120 - Planning and Administering Microsoft Azure for SAP Workloads
AZ-100-Deutsch - Microsoft Azure Infrastructure and Deployment (AZ-100 Deutsch Version)
AZ-301J - Microsoft Azure Architect Design (AZ-301日本語版)
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