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Last Updated: Aug 20, 2026

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DP-100 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our DP-100 exam dumps will include the following topics:

  • Perform feature engineering 15-20%
  • Develop models 40-45%
  • Prepare data for modeling 25-30%
  • Define and prepare the development environment 15-20%

The Microsoft DP-100 test helps candidates check their machine learning knowledge and skills and prove themselves as qualified data scientists.

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

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After obtaining the Microsoft Certified: Azure Data Scientist Associate certification, you become eligible for the data scientist role. Also, with this certification, you can expect to earn a decent remuneration of about $97k on average annually, as stated by PayScale.com. The expected career paths for a data scientist also include senior data scientist with a salary potential of $126,778 and data science manager with a salary potential of $136,781. Another possible path is that of a data science director with a potential pay of $157,095 annually. However, remember that besides, such a certificate work experience also matters.

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Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Deploy and retrain models10-15%- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows
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. 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%- Run experiments and train models
  • 1. Perform hyperparameter tuning
  • 2. Track experiments
  • 3. Use automated machine learning
- 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

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