DP-100 Practice Exam - Azure Data Scientist Certification Prep
DP-100 validates your expertise in designing and implementing data science solutions on Azure. Machine learning engineering is one of the highest-paid specializations in tech, and this certification proves you can build, train, and deploy ML models at scale.
Who should take DP-100
Data scientists working with Azure ML, ML engineers building production pipelines, analytics professionals moving into machine learning, and researchers deploying models to cloud environments.
What DP-100 covers
- Azure Machine Learning
- Data Exploration
- Model Training
- MLOps
- Responsible AI
Study tips for DP-100
- Practice building ML pipelines in Azure Machine Learning Studio and SDK v2
- Know AutoML capabilities, hyperparameter tuning, and model evaluation metrics
- Understand responsible AI tooling: Fairlearn, InterpretML, and error analysis
- Review MLOps concepts: model registration, deployment endpoints, and monitoring
DP-100 question bank
AzurePrep includes 350 DP-100 practice questions written to the published Microsoft skills outline. Questions span the full exam domain, not a recycled dump. Every question includes a detailed explanation and documentation reference so you understand why each answer is correct.
Frequently asked questions
How many questions are in the DP-100 practice exam?
The DP-100 practice exam covers Azure Machine Learning, data science workflows, model training, deployment, and MLOps practices. The current question count is shown on the DP-100 landing page.
What data science skills are required for DP-100?
DP-100 requires strong Python skills, experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch), statistics knowledge, and hands-on experience with Azure Machine Learning workspace.
Does DP-100 cover deep learning?
Yes, DP-100 covers both traditional machine learning and deep learning, including model training, hyperparameter tuning, distributed training, and deployment of deep learning models using Azure ML.