1Z0-1127-25 Using OCI Generative AI Service Practice Question
A healthcare company is deploying an OCI Generative AI service to summarize patient notes. They have recently moved from a managed serving endpoint to a dedicated AI cluster to ensure data privacy. The fine-tuned model is deployed on a dedicated cluster in the US West region. Users report that the summarization responses are now slower and occasionally timeout. The IT team checks the metrics: the cluster has 1 replica and CPU utilization is at 90%. The Object Storage bucket containing the model artifacts is in the same region. They have increased the timeout in their client configuration to 120 seconds, but still get timeouts. What should they do first to address the issue?
⚠ Common exam trap
The trap here is that candidates may focus on storage or client-side tuning (like timeout or token limits) instead of recognizing that a single overloaded replica is the root cause of performance degradation.
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 replicas in the dedicated cluster.
The dedicated AI cluster has only 1 replica and CPU utilization is at 90%, indicating that the single replica is overloaded and cannot handle the inference request volume. Increasing the number of replicas distributes the load, reduces latency, and prevents timeouts. This is the most direct and scalable fix for performance bottlenecks in a dedicated OCI Generative AI cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Move the Object Storage bucket to a local NVMe cache in the cluster.
Why it's wrong here
Dedicated clusters do not have local NVMe cache for Object Storage; model is loaded at startup.
- ✗
Move the model back to a managed serving endpoint in a different region.
Why it's wrong here
This would compromise data privacy and may not improve latency due to geographic distance.
- ✓
Increase the number of replicas in the dedicated cluster.
Why this is correct
Adding replicas provides more compute capacity to handle the load.
- ✗
Increase the max tokens parameter in the API call.
Why it's wrong here
Increasing max tokens will produce longer outputs, worsening performance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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