AWS Certified AI Practitioner AIF-C01 Practice Question
A team notices that a text foundation model hosted on Amazon Bedrock often returns lengthy answers that include extra, unrelated facts. Which prompt-engineering best practice will most directly help the team obtain only the information they need?
Raise the temperature setting to encourage more diverse token selection.
Rewrite the prompt to include clear, precise instructions that limit the scope and length of the response.
Insert several unrelated examples to make the prompt longer and more detailed.
Use zero-shot prompting without additional context or constraints.
When a prompt is specific and concise-for example, "List three advantages of Amazon Bedrock in one short sentence each" instead of "Tell me about Bedrock"-the model receives clear boundaries on scope and depth. This reduces ambiguity, limits the space for the model to digress, and therefore decreases the chance of verbose or off-topic content. Changing sampling parameters, adding unrelated examples, or removing context does not address the core problem that the instruction itself is too vague.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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