Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 128 of 306

Databricks Certified Generative AI Engineer Associate. Free level, Databricks. Free question with the correct answer and a full explanation.

Databricks Generative AI Engineer Associate Question 128

Select 3

You are tasked with adjusting a large language model's (LLM) response to provide more concise answers instead of verbose explanations. Which of the following prompt modifications would help you achieve this?

  1. A

    Add 'Be concise and brief in your response.' at the end of the prompt.

  2. B

    Start the prompt with 'Explain in detail with examples.'

  3. C

    Include 'Limit your response to 50 words or less.' in the prompt.

  4. D

    Use 'Provide a step-by-step explanation.' in the prompt.

  5. E

    Instruct the model with 'Respond in bullet points only.'

Show answer and explanation

Correct answers: A, C, E

Explanation

To adjust an LLM's response from a verbose baseline to a concise output, you need to use prompt modifications that explicitly guide the model towards brevity. Options like 'Be concise and brief,' 'Limit your response to 50 words or less,' and 'Respond in bullet points only' achieve this by providing clear instructions for conciseness or structural constraints. In contrast, prompts encouraging detailed explanations or examples lead to verbose outputs, which do not align with the desired outcome.

  • A. Correct.

    This modification explicitly instructs the model to be concise and brief, helping adjust the response to the desired output.

  • B. Incorrect.

    This modification encourages verbose explanations with examples, which is the opposite of the desired concise output.

  • C. Correct.

    This modification provides a clear constraint (50 words or less) to ensure brevity in the response.

  • D. Incorrect.

    This modification encourages detailed, step-by-step explanations, which contradicts the goal of conciseness.

  • E. Correct.

    This modification directs the model to use bullet points, which inherently promotes brevity and clarity.

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