1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data scientist is fine-tuning a generative AI model on OCI Data Science using a custom container with GPU resources. The training job fails with an out-of-memory error despite the GPU instance having sufficient memory. The job works fine on a smaller dataset. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the misconception that 'sufficient instance memory' guarantees no OOM errors, ignoring that GPU memory is a separate, finite resource that must accommodate both the model and the batch data simultaneously.
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
✓
The batch size is too large for the GPU memory
The most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The training script has a memory leak
Why it's wrong here
Memory leak would affect small dataset as well.
- ✗
The GPU instance is not supported by OCI Data Science
Why it's wrong here
GPU instances are supported.
- ✗
The model is not compatible with the PyTorch version
Why it's wrong here
Compatibility would cause errors regardless of dataset size.
- ✓
The batch size is too large for the GPU memory
Why this is correct
Large batch size can cause OOM errors; reducing batch size resolves it.
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