1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
You manage a generative AI model deployed on OCI Model Deployment that serves a chatbot application. The model is a 13B parameter LLM on a VM.GPU.A100.1 shape. Recently, you rolled out a new version of the model that is supposed to improve response quality. However, after the update, the application starts returning HTTP 500 errors and memory usage spikes. You need to update to the new version without causing downtime. The current deployment has 2 replicas with autoscaling enabled. Which strategy should you use to safely deploy the new model version?
⚠ Common exam trap
Many exam-takers assume increasing replicas provides safety through redundancy, but it does not prevent the new model from causing errors on all replicas; the key is isolation via a separate deployment and traffic shifting.
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
✓
Create a second deployment with the new model, test it, then shift traffic using a load balancer
It implements a blue/green deployment strategy: you create a second deployment with the new model, test it in isolation, and then shift traffic using a load balancer. This avoids downtime and allows you to validate the new model before exposing it to production traffic, which is critical given the observed HTTP 500 errors and memory spikes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Directly update the existing model deployment with the new model artifact
Why it's wrong here
This can cause immediate errors and service disruption.
- ✓
Create a second deployment with the new model, test it, then shift traffic using a load balancer
Why this is correct
Blue-green deployment ensures no downtime and safe rollout.
- ✗
Stop the existing deployment, update the model artifact, then start the deployment
Why it's wrong here
Stopping the deployment causes downtime.
- ✗
Increase the number of replicas to 4, then update the model
Why it's wrong here
This does not prevent the new model from causing errors; it only increases capacity.
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Senior Network & Security Engineer · founder of Courseiva
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