1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A team has deployed a generative AI model using OCI Data Science model deployment. The endpoint is behind a load balancer. Users report that after 5 minutes of inactivity, the first request takes over 30 seconds to respond, while subsequent requests are fast. What is the most likely cause and solution?
⚠ Common exam trap
Oracle often tests the distinction between infrastructure-level idle timeouts (which cause cold starts after inactivity) and application-level lazy initialization (which causes a one-time delay after deployment), and candidates may confuse the 5-minute inactivity pattern with a code initialization issue rather than a scaling policy.
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
✓
The model deployment has an idle timeout that scales down to zero; configure a minimum number of instances or use a warm-up request
The described behavior—first request after 5 minutes of inactivity taking over 30 seconds, with subsequent requests fast—is a classic symptom of an idle timeout that scales the model deployment to zero instances. OCI Data Science model deployments support auto-scaling with an idle timeout (default 5 minutes) that can reduce the number of instances to zero when no requests are received. When a new request arrives, it must wait for a new instance to spin up, causing the delay. The solution is to configure a minimum number of instances (e.g., 1) to keep the model warm, or use a warm-up request to prevent the idle timeout from triggering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model deployment has an idle timeout that scales down to zero; configure a minimum number of instances or use a warm-up request
Why this is correct
Idle timeout causes cold start; setting min replicas or health check warm-up solves it.
- ✗
The load balancer is scaling based on CPU utilization; increase the CPU threshold
Why it's wrong here
Load balancer scaling does not cause such pattern.
- ✗
The VCN has a network latency issue; use a different availability domain
Why it's wrong here
Latency would affect all requests, not just first.
- ✗
The inference code has a lazy initialization; pre-load the model in the deployment script
Why it's wrong here
Lazy init would cause cold start but the idle timeout is more likely the cause as it's a deployed service.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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