AWS Certified AI Practitioner AIF-C01 Practice Question
A development team is unfamiliar with a newly chosen large language model on Amazon Bedrock. To discover what the model can do, they want to probe its capabilities before committing to any design. Which prompt-engineering practice will best support this exploration?
Fine-tune the model immediately with domain-specific data.
Rely solely on negative prompts to constrain any unwanted content.
Force every request into a fixed prompt template with a strict output schema.
Issue broad zero-shot prompts first and iteratively adjust them based on the model's replies.
Beginning with broad, zero-shot prompts gives the team immediate, un-biased insight into the model's natural abilities. By reviewing the responses and then iteratively refining the wording, they can quickly map out strengths and weaknesses. Fine-tuning is costly and hides native skills, a rigid template stifles discovery, and using only negative prompts limits output rather than revealing possibilities.
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Why is fine-tuning not ideal for initial exploration?
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What are the risks of using rigid templates or negative prompts for exploration?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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