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AWS Certified Cloud Practitioner

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A data analyst uses Amazon Bedrock to generate financial summaries from quarterly reports. Sometimes, the model produces vague answers.

What should the analyst do to improve the output quality?

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RRitesh



Explanation:

Explanation

When using Amazon Bedrock or any generative AI service, the quality of the output is highly dependent on the quality of the input prompt. Here's why option B is correct:

  • Clearer instructions: Providing specific, detailed instructions helps the model understand exactly what you want
  • Defined output format: Specifying the structure (e.g., bullet points, tables, specific sections) ensures consistent and organized results
  • Context and constraints: Adding boundaries and requirements helps the model stay focused on the task

Why other options are incorrect:

  • A) Use a shorter prompt: Shorter prompts often lack necessary context and can lead to more vague responses
  • C) Randomize wording for creativity: This would likely make outputs less consistent and potentially more vague
  • D) Reduce temperature to zero: While this makes outputs more deterministic, it doesn't address the fundamental issue of unclear instructions

Best practices for prompt engineering:

  • Be specific about the desired format and structure
  • Provide clear examples if possible
  • Define any constraints or requirements
  • Use role-playing to guide the model's response style
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