AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 52

An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?

Answer options

• A. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
• B. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
• C. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
• D. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.

Correct answer: D

Explanation

The correct answer is D because SageMaker JumpStart provides pre-built models and solutions that align with a low-code no-code approach, making it suitable for fine-tuning LLMs. Options A, B, and C do not meet the LCNC criteria as they require more manual setup or are not designed for easy deployment within the specified framework.