1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data scientist wants to deploy a fine-tuned LLM on OCI for inference with low latency. Which OCI service should they use?
⚠ Common exam trap
It's easy for candidates to confuse development environments (Notebook Sessions) or general-purpose serverless compute (Functions) with purpose-built inference services, overlooking the need for GPU-accelerated, managed inference endpoints for low-latency LLM deployment.
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
✓
OCI Generative AI Service (Dedicated AI Cluster)
B is correct because OCI Generative AI Service with a Dedicated AI Cluster provides a managed, high-throughput, low-latency inference endpoint for fine-tuned LLMs. It leverages GPU-accelerated infrastructure and optimized serving stacks (e.g., vLLM, TensorRT-LLM) to minimize response times, making it ideal for production inference workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI Data Science Notebook Session
Why it's wrong here
Notebook sessions are interactive development environments, not production deployment.
- ✓
OCI Generative AI Service (Dedicated AI Cluster)
Why this is correct
Dedicated AI Cluster is optimized for low-latency inference with reserved resources.
- ✗
OCI Data Flow
Why it's wrong here
Data Flow is for batch data processing, not real-time model inference.
- ✗
OCI Functions
Why it's wrong here
Functions are for serverless code execution, not suitable for large model inference.
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