1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is using OCI Generative AI service with a dedicated AI cluster for text generation. They notice that the latency is higher than expected. The cluster is in the Ashburn region, and users are distributed globally. What is the most effective way to reduce latency?
⚠ Common exam trap
It's easy for candidates to confuse throughput improvements (scaling nodes or using an optimizer) with latency reduction, failing to recognize that geographic proximity is the only way to address network round-trip time for globally distributed users.
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
✓
Deploy dedicated AI clusters in regions closer to the users
Latency for globally distributed users is primarily driven by network distance and the speed of light. Deploying dedicated AI clusters in regions closer to the users reduces the physical distance data must travel, directly minimizing network round-trip time (RTT). This is the most effective architectural change because OCI's Generative AI service processes each request on the dedicated cluster and cannot bypass geographic latency through software optimizations alone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the OCI Generative AI inference optimizer
Why it's wrong here
No such optimizer exists.
- ✓
Deploy dedicated AI clusters in regions closer to the users
Why this is correct
Geographic proximity reduces network round-trip time.
- ✗
Increase the number of nodes in the dedicated AI cluster
Why it's wrong here
More nodes improve throughput but not per-request latency.
- ✗
Use a content delivery network (CDN) to cache responses
Why it's wrong here
CDN is for static content, not dynamic inference.
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