1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company has deployed a fine-tuned GPT model on OCI Generative AI using a dedicated AI cluster with 2 nodes. The endpoint is used by an internal application that generates product descriptions. Recently, the application started receiving timeouts and slow responses. The monitoring dashboard shows that the cluster's CPU utilization is consistently above 90%, and the request queue is growing. The team has verified that the model and code have not changed. The application traffic has increased by 20% over the past month. What should the team do to resolve the issue?
⚠ Common exam trap
Many exam-takers confuse reducing batch size (which actually increases CPU overhead per request) with reducing load, or assume caching is a universal performance fix, when the real solution is to scale the dedicated cluster horizontally to match increased traffic.
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 nodes in the dedicated AI cluster from 2 to 4.
The dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a serverless endpoint to handle variable traffic.
Why it's wrong here
Serverless endpoints may have cold starts and higher per-request latency, not ideal for sustained load.
- ✗
Reduce the batch size in the inference requests to lower CPU usage.
Why it's wrong here
Reducing batch size may lower per-request CPU but overall throughput decreases, worsening the queue.
- ✗
Implement a caching layer for frequently requested descriptions.
Why it's wrong here
Caching reduces redundant requests but doesn't address the underlying capacity shortage for unique requests.
- ✓
Increase the number of nodes in the dedicated AI cluster from 2 to 4.
Why this is correct
This directly adds compute capacity to handle the increased traffic.
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Senior Network & Security Engineer · founder of Courseiva
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