1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data scientist deployed a fine-tuned Llama 2 7B model on OCI Model Deployment with a single VM.GPU.A10.1 shape. Users report average latency of 3 seconds per request, which is too high for the intended real-time application. The model is used for short text generation (max 128 tokens). The data scientist wants to reduce per-request latency without significant accuracy loss. Which action would be most effective?
⚠ Common exam trap
Watch out — candidates often confuse throughput improvements (Option A) or perceived latency (Option C) with actual per-request latency reduction, or mistakenly think increasing max_tokens (Option B) would help, when in fact it worsens the problem.
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
✓
Apply 4-bit quantization using AWQ
4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of workers per replica
Why it's wrong here
More workers on a single GPU can lead to contention and may not reduce per-request latency.
- ✗
Increase the max_tokens parameter for the model
Why it's wrong here
Increasing max_tokens increases the output length, thereby increasing latency.
- ✗
Enable response streaming for the model endpoint
Why it's wrong here
Streaming allows partial output but does not reduce the total processing time.
- ✓
Apply 4-bit quantization using AWQ
Why this is correct
Quantization reduces model size and inference time with minimal accuracy loss.
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