AWS Certified AI Practitioner

Explanation

Correct Answer: C) SageMaker Studio

SageMaker Studio is the correct choice because it provides a fully managed, integrated development environment (IDE) specifically designed for machine learning workflows. Here's why:

Key Features of SageMaker Studio:

1. Managed IDE Environment: SageMaker Studio offers a web-based, fully managed IDE that includes Jupyter notebooks, debugging tools, and a comprehensive interface for ML development.
2. Model Training: It provides integrated tools for training machine learning models with SageMaker's built-in algorithms or custom code.
3. Performance Metrics Visualization: SageMaker Studio includes Experiment Tracking and Model Monitor features that allow users to visualize training metrics, compare experiments, and monitor model performance.
4. Collaboration: It supports real-time collaboration where multiple users can work on the same notebooks and share resources.
5. End-to-End ML Workflow: From data preparation to model deployment and monitoring, SageMaker Studio provides a unified interface.

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 for development and collaboration.
• 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. The correct answer should be C) SageMaker Studio as it specifically addresses the requirements for a managed IDE environment with training, visualization, and collaboration capabilities.