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AIF-C01 Practice Question: A financial services firm wants to deploy a…
A financial services firm wants to deploy a generative AI application that answers customer questions about account balances and recent transactions. The firm has strict latency requirements (responses under 2 seconds) and wants to minimize costs. Which strategy for model selection and deployment is MOST appropriate?
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
✓
Select a smaller, faster foundation model (e.g., Amazon Titan Text Lite) and use on-demand inference
For latency-sensitive and cost-conscious applications, selecting a smaller, faster model is preferable over a large model or custom deployment. Provisioned Throughput for dedicated capacity would increase cost and may not be needed if the base model performs adequately.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Select a smaller, faster foundation model (e.g., Amazon Titan Text Lite) and use on-demand inference
Why this is correct
A smaller model reduces latency and cost while still handling simple Q&A tasks well; on-demand avoids upfront costs.
- ✗
Use the largest available foundation model via on-demand inference for highest accuracy
Why it's wrong here
Largest models are slower and more expensive, likely exceeding latency targets and budget.
- ✗
Fine-tune a large model specifically on account data and deploy on a dedicated endpoint
Why it's wrong here
Fine-tuning and dedicated deployment are expensive and introduce latency; overkill for this use case.
- ✗
Deploy a large model using Provisioned Throughput to guarantee low latency
Why it's wrong here
Provisioned Throughput guarantees capacity but is costly and a large model may still be slower than needed.
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