AIF-C01 · Question #366
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared. Which solution will meet these requirements with the LEAST…
The correct answer is B. Use Amazon SageMaker JumpStart to create a training job. SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.
Question
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared. Which solution will meet these requirements with the LEAST operational effort?
Options
- ACreate a custom model training job in PartyRock on Amazon Bedrock.
- BUse Amazon SageMaker JumpStart to create a training job.
- CUse a custom script to run an Amazon SageMaker AI model training job.
- DCreate a Jupyter notebook on an Amazon EC2 instance. Use the notebook to train the model.
How the community answered
(21 responses)- A24% (5)
- B57% (12)
- C5% (1)
- D14% (3)
Explanation
SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.
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