Fundamentals of Large Language Models →easyMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A team wants to deploy an LLM for real-time inference with low latency. Which OCI deployment option is best?

⚠ Common exam trap

Many exam-takers confuse OCI Functions (a serverless compute service) with a viable inference platform, overlooking the GPU requirement for LLM workloads, or mistakenly think OCI Streaming can process inference requests because of its 'real-time' label.

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 Data Science Model Deployment with GPU shapes

OCI Data Science Model Deployment with GPU shapes is the best option because it provides managed, scalable, low-latency inference endpoints for LLMs. GPU shapes (e.g., VM.GPU.A10) are essential for the parallel matrix computations required by transformer-based models, and the deployment service supports auto-scaling and load balancing to maintain real-time response times.

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 Model Deployment with GPU shapes
Why this is correct
GPU shapes provide the compute power needed for low-latency LLM inference.
✗
OCI Functions with CPU
Why it's wrong here
CPU-based functions are not suitable for LLM inference due to high computational requirements.
✗
OCI Events
Why it's wrong here
Events service is for event-driven automation, not for inference.
✗
OCI Streaming
Why it's wrong here
Streaming is for real-time data ingestion and processing, not model inference.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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