AIF-C01 Fundamentals of Generative AI Practice Question
A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?
⚠ Common exam trap
AWS often tests the distinction between model capacity limits (context window) and output quality parameters (temperature, prompt engineering), leading candidates to incorrectly attribute omission errors to randomness or lack of examples rather than the fundamental constraint of input size.
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
✓
The model's context window is too small
When summarizing long documents with Amazon Bedrock, the model's context window determines the maximum amount of text it can process at once. If the document exceeds this limit, the model truncates or ignores portions, leading to omitted key details. This is the most likely cause because summarization requires the model to attend to the entire input, and a small context window directly prevents full coverage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is overfitted
Why it's wrong here
Overfitting concerns training-data generalisation, not summarisation completeness. Omission of key details typically stems from input length exceeding the context window or truncation, so content is dropped before the model sees it. Overfitting would be the cause when a model performs well on training data but poorly on new data.
- ✗
The prompt lacks examples
Why it's wrong here
Few-shot examples shape output format and tone, not coverage of source content, so they cannot stop a summariser dropping details from a long document. Examples are the right choice when the model must imitate a specific style or structure, such as a fixed report template.
- ✓
The model's context window is too small
Why this is correct
Summarisation requires the whole document plus the prompt to fit within the model's context window. When the input exceeds that token limit, content is truncated before inference, so later sections never reach the model and their details are omitted from the summary.
- ✗
The temperature parameter is too high
Why it's wrong here
Temperature controls randomness in token selection; lowering it makes output more deterministic but does not improve recall of document content. Temperature tuning is correct when answers must be reproducible or creative variation is unwanted, not when summarisation omits source details.
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