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 affects randomness, not output length; higher temperature may increase variability but not completeness.
- ✗
Increase the top_p parameter
Why it's wrong here
Top_p (nucleus sampling) controls probability mass for token selection, not length.
- ✓
Increase the maximum token count
Why this is correct
Max tokens sets a hard limit on the number of tokens generated; raising it allows longer responses.
- ✗
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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