Microsoft Dp-100 Azure data scientist Practice Exam
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Start NowAbout the Microsoft Dp-100 Azure data scientist Certification
The Microsoft Dp-100 Azure data scientist certification is designed for professionals who apply data science and machine learning techniques to Azure workloads using Python, Azure Machine Learning, and MLflow. they design experiments, train models, and deploy scalable AI solutions. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using Microsoft tools and frameworks. Earning the certification positions you as a trusted expert, capable of solving high-impact challenges and contributing to secure, scalable, and efficient systems.
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Test Your Knowledge of Microsoft Dp-100 Azure data scientist
Question #1
A data scientist needs to train a model using a large dataset stored in Azure Blob Storage.
Which Azure service should be used to manage the training environment and resources?
A) Azure Machine Learning
B) Azure Synapse Analytics
C) Azure Data Factory
D) Azure Databricks
Solution
Correct answers: A – Explanation:
Azure Machine Learning provides managed compute environments and tools for training models using data from Azure Blob Storage.
Question #2
A team wants to automate hyperparameter tuning for a classification model.
Which Azure feature supports this task?
A) Sweep configuration in Azure ML
B) Azure Monitor
C) Azure Data Lake
D) Azure Functions
Solution
Correct answers: A – Explanation:
Sweep configuration in Azure ML enables automated hyperparameter tuning to optimize model performance.
Question #3
A company needs to deploy a trained model as a REST API endpoint.
Which Azure service should be used?
A) Azure Machine Learning Managed Online Endpoint
B) Azure App Service
C) Azure Logic Apps
D) Azure Functions
Solution
Correct answers: A – Explanation:
Azure ML Managed Online Endpoint allows deployment of models as scalable REST APIs for real-time inference.
Question #4
A data scientist wants to track model performance over time and detect data drift.
Which Azure feature supports this requirement?
A) Model monitoring in Azure ML
B) Azure Policy
C) Azure Monitor
D) Azure Data Factory
Solution
Correct answers: A – Explanation:
Model monitoring in Azure ML tracks metrics and detects data drift to maintain model accuracy.
Question #5
A team needs to run experiments using different algorithms and compare results.
Which Azure ML feature supports this?
A) Experiment tracking
B) Azure Monitor
C) Azure Synapse Studio
D) Azure Data Explorer
Solution
Correct answers: A – Explanation:
Experiment tracking in Azure ML logs runs, parameters, and metrics to compare algorithm performance.
Question #6
A company wants to use AutoML to identify the best model for a regression task.
Which Azure service provides this capability?
A) Azure Machine Learning
B) Azure Data Factory
C) Azure Synapse Analytics
D) Azure App Service
Solution
Correct answers: A – Explanation:
Azure Machine Learning includes AutoML features that automate model selection and tuning for regression tasks.
Question #7
A data scientist needs to register a trained model for reuse and deployment.
Which Azure ML component should be used?
A) Model registry
B) Azure Key Vault
C) Azure Monitor
D) Azure Databricks
Solution
Correct answers: A – Explanation:
The model registry in Azure ML stores trained models for versioning, reuse, and deployment.
Question #8
A team wants to use notebooks to explore data and build models collaboratively.
Which Azure ML feature supports this?
A) Azure ML Notebooks
B) Azure Monitor
C) Azure Data Factory
D) Azure Functions
Solution
Correct answers: A – Explanation:
Azure Machine Learning provides managed compute environments and tools for training models using data from Azure Blob Storage.
Question #9
A company wants to schedule retraining of models based on new data availability.
Which Azure service supports this automation?
A) Azure Machine Learning pipelines
B) Azure Monitor
C) Azure Policy
D) Azure App Configuration
Solution
Correct answers: A – Explanation:
Azure ML pipelines automate workflows including data preparation, training, and deployment based on triggers.
Question #10
A data scientist needs to optimize a language model using prompt engineering and RAG techniques.
Which Azure ML capability supports this?
A) Prompt Flow
B) Azure Synapse Studio
C) Azure Data Factory
D) Azure App Service
Solution
Correct answers: A – Explanation:
Prompt Flow in Azure ML enables optimization of language models using prompt engineering and Retrieval Augmented Generation.
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