AIF-C01 Fundamentals of Generative AI Practice Question
Which TWO factors are most important when selecting a foundation model in Amazon Bedrock for a text summarization task with strict latency requirements?
⚠ Common exam trap
A common misconception is that model size (parameters) is the primary driver of latency, but in practice, latency depends on inference optimization, model quantization, and hardware, not just parameter count.
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
✓
Average response latency per request.
Average response latency per request directly measures how quickly the model generates summaries, which is critical for strict latency requirements. Amazon Bedrock provides latency metrics for each foundation model, and selecting a model with lower average latency ensures the summarization task meets performance SLAs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Average response latency per request.
Why this is correct
Low latency is critical for real-time summarization.
- ✗
Model size in billions of parameters.
Why it's wrong here
Model size affects latency but latency itself is the direct metric.
- ✗
Maximum input token limit.
Why it's wrong here
Important but secondary to latency and quality.
- ✓
Output quality and token efficiency for summarization tasks.
Why this is correct
High-quality, concise summaries are the primary goal.
- ✗
Availability of fine-tuning capability for domain adaptation.
Why it's wrong here
Fine-tuning not required for this use case and adds latency.
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