1Z0-1127-25 OCI Generative AI Service Practice Question
A company has fine-tuned a Cohere Command R model using T-Few and wants to deploy it for real-time inference with the lowest possible latency. They have provisioned a dedicated AI cluster with 2 model units. However, latency is still higher than expected. Which action is MOST likely to reduce latency?
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
✓
Increase the number of model units on the dedicated AI cluster
Increasing model units on the dedicated cluster provides more compute capacity, reducing inference latency by parallelizing requests. Switching to shared infrastructure would likely increase latency due to multi-tenancy. Using a larger model would increase latency. Reducing temperature does not affect latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the temperature parameter to 0
Why it's wrong here
Temperature does not affect inference latency; it only influences output randomness.
- ✓
Increase the number of model units on the dedicated AI cluster
Why this is correct
Increasing the number of model units from two to a higher count distributes the inference workload across more parallel compute resources, directly addressing the bottleneck of insufficient throughput for real-time requests. This action satisfies the constraint of achieving the lowest possible latency by reducing queue wait time per request, as the dedicated AI cluster’s capacity is currently under-provisioned for the demand.
- ✗
Switch from dedicated AI cluster to shared infrastructure
Why it's wrong here
Shared infrastructure typically has higher latency due to contention.
- ✗
Use a larger base model like Llama 3 70B
Why it's wrong here
Larger models have higher inference latency.
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