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AIF-C01 Practice Question: Using Amazon Bedrock to build an application that…
A company is using Amazon Bedrock to build an application that requires very low latency responses (under 100ms). They are currently using a large model but need faster inference. Which model selection strategy is MOST appropriate?
⚠ Common exam trap
A common misconception in AWS exams is that adjusting hyperparameters like temperature can affect inference speed, but temperature only influences output diversity and randomness.
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
✓
Switch to a smaller model that can meet the latency requirement while still providing acceptable quality
Smaller models have fewer parameters, which reduces the computational cost per inference, directly lowering latency. For latency-sensitive applications requiring under 100ms responses, a smaller model can often provide acceptable quality while meeting the strict timing requirement, whereas a larger model would introduce higher inference latency due to increased matrix operations and memory bandwidth demands.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter to speed up generation
Why it's wrong here
Temperature governs sampling randomness, not inference speed; it cannot reduce the time a forward pass takes, so the 100ms target stays unmet. Temperature tuning belongs to creativity control, such as diversifying marketing copy, not to latency optimisation.
- ✗
Use a larger model with more parameters for better accuracy
Why it's wrong here
Adding parameters increases compute per token, raising latency further and moving away from the sub-100ms requirement. Larger models are chosen when accuracy on complex reasoning matters more than response time, not when inference speed is the binding constraint.
- ✓
Switch to a smaller model that can meet the latency requirement while still providing acceptable quality
Why this is correct
Smaller models have fewer parameters, so each forward pass requires less computation and memory bandwidth, cutting inference latency. This satisfies the sub-100ms constraint while retaining acceptable output quality, unlike prompt tuning or provisioned throughput, which do not reduce per-token compute.
- ✗
Use batch processing instead of real-time streaming
Why it's wrong here
Batch processing defers inference to asynchronous jobs, so responses cannot arrive within 100ms; it targets throughput over latency. It is tempting because Amazon Bedrock batch inference suits large offline workloads, such as overnight document classification, where turnaround time is measured in hours and per-token cost matters more than immediate replies.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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