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.
Foundation models have a fixed maximum context window (e.g., 4,096 tokens for GPT-3.5). By breaking a long document into smaller chunks and summarizing each independently, you avoid truncation and ensure the model can process the entire content without losing information. This chunking strategy is a standard preprocessing technique for handling documents that exceed the model's context length.
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 allows handling of long documents that exceed context length.
- ✗
Use a high temperature parameter to increase creativity.
Why it's wrong here
High temperature makes output more random and less focused.
- ✓
Provide few-shot examples of desired summaries in the prompt.
Why this is correct
Few-shot examples help the model understand the expected output format and style.
- ✗
Use a low frequency penalty to reduce repetition.
Why it's wrong here
Frequency penalty addresses repetition, not summary quality.
- ✗
Use a longer context length by increasing the max tokens parameter.
Why it's wrong here
Increasing max tokens does not help if the document exceeds the model's context window.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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