MLA-C01 · Question #152
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…
The correct answer is A. Use SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real-. SageMaker Training Compiler accelerates training of large models like LLMs by optimizing GPU utilization, making it suitable for efficient large-scale training. For deployment of a live conversational engine that requires real-time responses, the correct choice is a SageMaker…
Question
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?
Options
- AUse SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real-
- BUse SageMaker with deep learning containers for large model inference to train the LLM. Deploy
- CUse SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker
- DUse SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.
How the community answered
(21 responses)- A76% (16)
- B5% (1)
- C5% (1)
- D14% (3)
Explanation
SageMaker Training Compiler accelerates training of large models like LLMs by optimizing GPU utilization, making it suitable for efficient large-scale training. For deployment of a live conversational engine that requires real-time responses, the correct choice is a SageMaker real- time inference endpoint. This combination meets both training and deployment requirements
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