AI-900 Question 135
Select 2A data scientist is working on a machine learning project that requires scalable compute resources and integration with various machine learning frameworks. Which Azure services are most suitable for this scenario?
- A
Azure Machine Learning
- B
Azure Databricks
- C
Azure Cognitive Services
- D
Azure Virtual Machines
- E
Azure Synapse Analytics
Show answer and explanation
Correct answers: A, B
Explanation
Azure Machine Learning and Azure Databricks are both tailored for machine learning and data science workloads. Azure Machine Learning provides an end-to-end environment for model development and deployment, while Azure Databricks is optimized for big data and machine learning, offering scalable compute and integration with popular frameworks. These services are ideal for scalable and flexible machine learning projects.
- A. Correct.
Azure Machine Learning is a comprehensive platform that supports the end-to-end machine learning lifecycle, including training and deploying models using scalable compute resources. It integrates with popular ML frameworks like TensorFlow and PyTorch.
- B. Correct.
Azure Databricks is an analytics platform optimized for big data and machine learning workloads. It provides scalable resources and integration with frameworks like Spark MLlib and other machine learning tools.
- C. Incorrect.
Azure Cognitive Services provides pre-built AI models for tasks like vision, speech, and language, but it is not designed for custom machine learning model development or training.
- D. Incorrect.
Azure Virtual Machines can provide compute resources, but they lack the specialized features and integrations offered by Azure Machine Learning and Azure Databricks for data science and machine learning projects.
- E. Incorrect.
Azure Synapse Analytics is primarily designed for big data analytics and data warehousing, not for machine learning training or integration with ML frameworks.