1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company deploys a fine-tuned Llama 2 model using OCI Generative AI service. They want to ensure low-latency inference for a real-time chat application. Which deployment option should they use?
⚠ Common exam trap
Watch out — candidates often confuse 'serverless endpoint (standard)' with a low-latency option, not realizing that its shared infrastructure and potential cold starts make it unsuitable for real-time inference, while a dedicated cluster guarantees consistent performance.
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
✓
Dedicated AI cluster
A dedicated AI cluster provides reserved compute resources (GPUs) for low-latency, real-time inference by eliminating resource contention. This is essential for a fine-tuned Llama 2 model in a chat application where consistent sub-second response times are required, unlike shared or serverless options that introduce cold starts or queuing delays.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch inference job
Why it's wrong here
Batch inference is designed for processing large datasets, not real-time requests.
- ✗
OCI Functions
Why it's wrong here
OCI Functions are for short-lived stateless functions, not for running large models.
- ✓
Dedicated AI cluster
Why this is correct
Dedicated AI clusters offer reserved capacity and low latency for real-time inference.
- ✗
Serverless endpoint (standard)
Why it's wrong here
Serverless endpoints have cold starts and variable latency, not ideal for real-time chat.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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