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AIF-C01 Practice Question: Deploying a generative AI application using…
A company is deploying a generative AI application using Amazon Bedrock and needs to optimize costs for a high-volume, latency-tolerant workload. Which TWO strategies should they implement? (Select TWO.)
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
✓
Use Batch Inference for asynchronous processing
Using a smaller model reduces per-request cost, and batch inference processes large volumes asynchronously at lower cost. Provisioned Throughput increases cost; caching helps but is not a primary cost optimization for high-volume; fine-tuning adds cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Batch Inference for asynchronous processing
Why this is correct
Batch inference is cheaper per request and suitable for non-real-time workloads.
- ✗
Deploy a large model and fine-tune it
Why it's wrong here
Fine-tuning adds upfront cost and using a large model increases per-request cost.
- ✓
Use a smaller, more efficient foundation model
Why this is correct
Smaller models are cheaper per inference and often sufficient for many tasks.
- ✗
Enable Provisioned Throughput for guaranteed capacity
Why it's wrong here
Provisioned Throughput has higher cost for dedicated capacity; not cost-optimal for latency-tolerant workloads.
- ✗
Implement model caching to avoid redundant inferences
Why it's wrong here
Caching is beneficial but not a primary cost optimization strategy for high-volume, varied requests; it's more for repeated queries.
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