1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A team is fine-tuning a generative AI model on OCI using a custom dataset. The training job fails with an out-of-memory error. Which THREE actions should they take to resolve this issue?
⚠ Common exam trap
Oracle often tests the misconception that increasing the learning rate or epochs can resolve memory errors, when in fact only actions that directly reduce per-step memory footprint (like reducing batch size, using gradient accumulation, or upgrading to a larger GPU) are effective.
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
✓
Use gradient accumulation to simulate larger batch sizes.
Gradient accumulation allows the model to simulate the effect of a larger batch size without increasing memory usage. Instead of computing gradients over a single large batch, the optimizer accumulates gradients over several smaller batches before performing a weight update. This technique effectively decouples the batch size from memory consumption, enabling training on large models or high-resolution inputs that would otherwise cause an out-of-memory error.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use gradient accumulation to simulate larger batch sizes.
Why this is correct
Gradient accumulation allows effective large batches with less memory.
- ✗
Increase the learning rate to speed up training.
Why it's wrong here
Learning rate does not affect memory usage.
- ✓
Use a larger GPU shape with more memory.
Why this is correct
Upgrading to a larger GPU provides more memory to accommodate the workload.
- ✓
Reduce the batch size.
Why this is correct
Smaller batch size reduces memory consumption per iteration.
- ✗
Increase the number of training epochs.
Why it's wrong here
More epochs increase training time but not peak memory.
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