Amazon Web Services AWS Certified AI Practitioner Exam AIF-C01 Question # 1 Topic 1 Discussion
AIF-C01 Exam Topic 1 Question 1 Discussion:
Question #: 1
Topic #: 1
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
A.
Provide labeled data with the prompt field and the completion field.
B.
Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
C.
Purchase Provisioned Throughput for Amazon Bedrock.
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
Fine-Tuning Strategy:
To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
This allows the model to learn the desired behavior or response style based on the provided examples.
Why Option A is Correct:
Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
Customization: Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.
Why Other Options are Incorrect:
B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
D. Train on journals and textbooks: Lacks the specific format and labeling required for fine-tuning.
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