The answer is to create a second deployment with the new model, test it, then shift traffic using a load balancer. This blue/green deployment strategy is correct because it isolates the problematic new model version in a separate environment, allowing you to validate its behavior—especially critical given the HTTP 500 errors and memory spikes—before routing any production traffic to it. By keeping the original deployment active and only switching the load balancer after successful testing, you achieve a true zero-downtime model update for OCI deployment. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding of safe deployment patterns for GPU-backed LLMs, where rolling updates can cause cascading failures due to resource contention. A common trap is assuming autoscaling will absorb the new model’s memory spike, but autoscaling cannot fix a faulty model—it only adds more replicas of the same broken code. Memory tip: think “Blue/Green for GPU” to remember that new model versions on expensive A100 shapes always need a separate staging ground before traffic shift.
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Blue/green deployment on OCI Model Deployment uses separate deployments behind a load balancer; you can test the green deployment with a small percentage of traffic (e.g., via weighted routing) before fully switching. The HTTP 500 errors and memory spikes suggest the new model may have a memory leak or inefficient inference path, which would be caught during the testing phase. OCI Load Balancer supports session persistence and health checks, ensuring traffic is only routed to healthy replicas.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a second deployment with the new model, test it, then shift traffic using a load balancer — Option B is correct because 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.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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