AIF-C01 Applications of Foundation Models Practice Question
A data scientist is using a foundation model to summarize long documents. Which TWO of the following steps are most likely to improve the quality of the summaries?
⚠ Common exam trap
AWS often tests the misconception that increasing max tokens extends the model's input capacity, when in reality it only controls the output length, while the input is constrained by the model's inherent context window.
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
✓
Break the input document into chunks and summarize each chunk separately.
Option A is correct because chunking a long document and summarizing each chunk separately keeps each request within the model's effective context window, avoiding truncation and the degraded recall that occurs when a foundation model must attend to very long inputs, and the chunk summaries can then be combined into a final summary. Option C is correct because few-shot prompting supplies concrete examples of the desired summary style, length, and level of detail, which steers the model's output distribution toward the target format more reliably than a bare instruction. Option B is not appropriate because a high temperature increases randomness and creativity, which harms factual fidelity and consistency in summarization. Option D is not the priority here because frequency penalty only discourages repeated tokens and does not address the core problems of long-input context limits or output style alignment. Option E is not correct because increasing max tokens only raises the output length cap; it does not extend the model's usable context window for the input document and can even encourage overly long, unfocused summaries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Break the input document into chunks and summarize each chunk separately.
Why this is correct
Chunking splits the long document into segments that fit the model's context window, letting each chunk be summarised without truncation. This satisfies the long-document constraint, since whole-document input would exceed context limits and lose detail, degrading summary quality.
- ✗
Use a high temperature parameter to increase creativity.
Why it's wrong here
Higher temperature flattens the probability distribution, increasing randomness and hallucination risk in a task demanding faithful extraction. It is tempting because temperature legitimately governs creativity, and would be correct for brainstorming or generating varied marketing copy, not for grounded summarisation of factual documents.
- ✓
Provide few-shot examples of desired summaries in the prompt.
Why this is correct
Few-shot examples demonstrate the desired summary style, length and structure directly in the prompt, steering the foundation model's output distribution toward the target format. This satisfies the quality requirement without retraining, since the model conditions on the supplied exemplars at inference time.
- ✗
Use a low frequency penalty to reduce repetition.
Why it's wrong here
Frequency penalty adjusts token repetition during decoding; it does not improve factual coverage or fidelity, and a low value barely alters output. It belongs in creative or conversational generation where looping phrases must be suppressed. Summarisation quality improves through prompt engineering, retrieval of source context, or fine-tuning, not repetition control.
- ✗
Use a longer context length by increasing the max tokens parameter.
Why it's wrong here
Raising max tokens only lets the model emit longer output; it does not extend the input window, so the document is still truncated before summarisation. It is tempting because max tokens genuinely controls generation length, and would be correct when summaries are being cut off mid-sentence rather than when source text is being dropped.
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Written by Johnson Ajibi, MSc IT Security
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