AIF-C01 Fundamentals of Generative AI Practice Question
A data scientist fine-tuned a large language model on Amazon SageMaker for financial report generation. The model produces responses that are too short and incomplete, often cutting off mid-sentence. What parameter should be adjusted first?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the maximum token count
The max tokens parameter limits the length of generated responses. Increasing it allows the model to produce longer completions. Temperature, top_p, and model change affect quality or diversity, but not the length cap.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter
Why it's wrong here
Temperature scales sampling randomness; raising it makes wording more varied, not longer, so mid-sentence truncation continues. It is tempting because it is the most familiar generation knob, yet output length is governed by max_new_tokens or max_length, which must be increased to stop cut-offs.
- ✗
Increase the top_p parameter
Why it's wrong here
top_p narrows the nucleus of tokens considered, which can truncate output rather than extend it, so cut-off sentences persist. It is tempting because it controls randomness, but the parameter governing response length is max_new_tokens or max_length, which should be raised first.
- ✓
Increase the maximum token count
Why this is correct
Truncated, mid-sentence output indicates generation stopped at the configured output limit rather than the model finishing naturally. Raising the maximum token count lets the model complete longer financial reports, directly addressing the premature cutoff constraint in the stem.
- ✗
Switch to a different foundation model
Why it's wrong here
Switching foundation models does not address the root cause of truncated outputs, which is a decoding parameter like `max_new_tokens` or `max_length` that caps sequence length; a different model with the same token limit would still cut off mid-sentence. It is tempting because changing the model can resolve issues like poor domain-specific accuracy or hallucination, and would be correct if the problem were factual errors or irrelevant content rather than incomplete generation.
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