AIF-C01 Fundamentals of Generative AI Practice Question
A financial services firm fine-tuned a generative AI model on Amazon SageMaker to summarize quarterly reports. The summaries often miss key financial metrics such as revenue and profit margins. The fine-tuning dataset contained full reports with summaries that included these metrics. The model appears to understand the reports but omits critical numbers. Which course of action would most likely improve the summaries?
⚠ Common exam trap
AWS often tests the misconception that increasing output length or switching models will fix content omission, when the real solution lies in improving the fine-tuning data quality and instruction design.
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
✓
Re-fine-tune using a carefully crafted dataset that includes explicit instructions to include key metrics and provides examples of correct summaries
The model's failure to include key financial metrics despite having them in the training data indicates a misalignment between the training objective and the desired output. By re-fine-tuning with a dataset that explicitly instructs the model to include key metrics and provides correct examples, you directly teach the model to prioritize and extract those specific numerical values during summarization, addressing the root cause of omission.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Re-fine-tune using a carefully crafted dataset that includes explicit instructions to include key metrics and provides examples of correct summaries
Why this is correct
The dataset lacked supervision signalling that metrics matter, so the model learned to omit them. Re-fine-tuning with explicit instructions and exemplar summaries that retain revenue and profit figures supplies that signal, steering generation toward including critical numbers.
- ✗
Increase the maximum number of tokens in the summary
Why it's wrong here
Longer summaries may include more content but do not ensure critical metrics are prioritized.
- ✗
Switch to a different pre-trained model like Claude instead of the current one
Why it's wrong here
Swapping the base model discards the domain fine-tuning that already taught the model report structure; the omission stems from training data weighting, not model family. Claude suits general reasoning tasks without labelled examples, but here the labelled dataset exists and needs rebalancing toward metric-bearing summaries.
- ✗
Implement a post-processing Lambda function that extracts metrics from the original report and appends them to the summary
Why it's wrong here
A Lambda appending extracted metrics bypasses the model rather than teaching it to prioritise them, and extraction logic cannot reliably identify every relevant figure. It is tempting because it guarantees the numbers appear, and it would be correct if the model genuinely could not access the source data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.