1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?
⚠ Common exam trap
The trap here is that candidates might assume a 500MB model is 'small enough' for CPU inference, overlooking that transformer architecture—not model size—is the primary driver of latency, and that GPU acceleration is essential even for moderately sized transformer models.
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
✓
Missing GPU acceleration for inference
GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
High network latency between the client and the model endpoint
Why it's wrong here
Network latency is verified minimal (<5ms), so not the cause.
- ✗
Model is too large for the VM.Standard.E3.Flex shape
Why it's wrong here
The model size (500MB) is well within the 16GB RAM of the shape.
- ✗
Insufficient CPU resources for the model size
Why it's wrong here
CPU resources are adequate (2 OCPU) but the bottleneck is the lack of GPU for parallel computation.
- ✓
Missing GPU acceleration for inference
Why this is correct
GPU acceleration is essential for fast inference on neural network models like GPT-2.
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