AWS Certified Cloud Practitioner
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Q5. A machine learning engineer wants a managed IDE environment to train models, visualize performance metrics, and collaborate with peers. Which SageMaker capability should they use?
Real Exam
Community
RRitesh
0
A
SageMaker Ground Truth
B
SageMaker Pipelines
C
SageMaker Studio
D
SageMaker Canvas
Explanation:
Explanation
Correct Answer: C) SageMaker Studio
Why SageMaker Studio is correct:
- SageMaker Studio is Amazon's fully integrated development environment (IDE) for machine learning
- It provides a web-based, collaborative IDE for ML workflows
- Features include: notebook-based development, model training, visualization tools, debugging, and collaboration capabilities
- Specifically designed for ML engineers to train models, visualize performance metrics, and collaborate with peers
Why other options are incorrect:
- A) SageMaker Ground Truth: This is a data labeling service for creating high-quality training datasets, not an IDE environment
- B) SageMaker Pipelines: This is a CI/CD service for automating ML workflows, not a managed IDE
- D) SageMaker Canvas: This is a no-code visual interface for business analysts to build ML models without writing code, not a full IDE for ML engineers
Note: The provided answer in the text says "Ans : b" which appears to be incorrect. Based on AWS SageMaker capabilities, SageMaker Studio is the correct choice for a managed IDE environment for ML engineers.
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