AIF-C01 Fundamentals of Generative AI Practice Question
A solutions architect must choose a foundation model for an application that summarizes lengthy internal audit reports. The reports average 60,000 tokens, and the summaries must reflect details from the beginning, middle, and end of each document. Cost per request matters, but recall of details is the top priority. Which model characteristic should drive the selection?
⚠ Common exam trap
The trap here is equating a larger parameter count with the ability to handle longer inputs, when context window size is a separate and independently configured limit.
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 maximum context window length
When a single request must contain an entire long document, the maximum context window is the hard constraint that decides feasibility. Parameter count, output modality, and latency influence quality or experience but do not determine whether 60,000 tokens can be processed without losing the details the task requires.
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's inference latency percentile
Why it's wrong here
Latency affects user experience and throughput planning, and it is worth considering, but it does not determine whether the document fits at all. A fast model that truncates the report produces fast wrong answers. Recall of details is governed by context capacity and quality, not by speed.
- ✗
The number of parameters in the model
Why it's wrong here
Parameter count correlates loosely with capability but says nothing about how much text a single request can hold. A very large model with a short context window still cannot ingest a 60,000-token report in one pass. Sizing decisions based on parameters alone would miss the binding constraint in this scenario.
- ✗
The model's supported output modalities
Why it's wrong here
Output modality matters when you need images, audio, or structured media, but this task produces text summaries. Whether the model can also emit images or speech has no bearing on its ability to read a long report and preserve details from every section, so it is not the deciding factor.
- ✓
The model's maximum context window length
Why this is correct
A document of roughly 60,000 tokens must fit inside the model's context window along with the prompt and the generated summary. If the window is smaller, content must be truncated or chunked, which risks losing the details the team cares about most. Context window length is therefore the gating characteristic for this workload.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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