1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Your organization has deployed a generative AI model for a multilingual translation service on OCI Model Deployment. The model is a 13B parameter transformer hosted on a single VM.GPU.A100.1 shape with 2 replicas. Recently, the service experiences intermittent timeouts when a burst of requests arrives. You have enabled autoscaling based on CPU utilization, but the scaling is too slow. After investigation, you find that the model inference time is highly variable due to different sequence lengths. You need to ensure the service can handle sudden spikes without timeouts. Which solution should you implement?
⚠ Common exam trap
Many candidates assume autoscaling (option B or D) is sufficient for burst handling, but they overlook that autoscaling has inherent latency (minutes to provision new replicas), whereas a request queue provides immediate buffering to absorb spikes without dropping requests.
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
✓
Implement a request queue (e.g., OCI Queue) to buffer requests and process them asynchronously
Implementing a request queue (e.g., OCI Queue) decouples request ingestion from processing, allowing the service to buffer bursts of requests and process them asynchronously. This prevents timeouts by smoothing out the variable inference times caused by differing sequence lengths, as the queue absorbs spikes and the model processes at its own pace. Autoscaling based on CPU utilization is too slow for sudden spikes, but a queue provides immediate relief by not dropping requests.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a request queue (e.g., OCI Queue) to buffer requests and process them asynchronously
Why this is correct
Queuing decouples traffic spikes from the model, preventing timeouts.
- ✗
Increase the maximum number of replicas and prewarm additional replicas before expected traffic
Why it's wrong here
Prewarming is not dynamic and may not cover unpredictable spikes.
- ✗
Reduce the model size to a 7B parameter model to decrease inference time
Why it's wrong here
This reduces accuracy and may not be acceptable for the translation service.
- ✗
Use autoscaling based on the number of messages in the request queue
Why it's wrong here
This still has scaling latency and may not prevent immediate timeouts.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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