AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 138
A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credit card data.
An ML engineer must implement a solution to train and deploy the LLM on Amazon SageMaker.
Which solution will meet these requirements?
Answer options
- A. Use SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real-time inference.
- B. Use SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference.
- C. Use SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker Asynchronous Inference.
- D. Use SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.
Correct answer: A
Explanation
The correct answer is A because SageMaker Training Compiler optimizes training for large models like LLMs, and real-time inference allows for immediate responses, which is essential for a live conversational engine. Options B, C, and D do not utilize the optimal training methods or real-time deployment needed for the LLM in this scenario.