MLS-C01 Practice Question: Machine Learning Implementation and Operations
During training of a deep learning model on a GPU instance in SageMaker, the training job fails with an insufficient memory error. Which step should be taken first to resolve this issue?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that hyperparameter tuning (learning rate) or regularization (dropout) can fix memory errors, when in fact only batch size or model size directly control VRAM usage.
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
✓
Reduce the batch size
The most direct cause of an out-of-memory (OOM) error during GPU training is that the combined size of the model parameters, activations, and gradients exceeds the GPU's VRAM. Reducing the batch size immediately decreases the memory footprint of activations stored for backpropagation, which is the largest and most tunable memory consumer. This is the first and simplest step to resolve the error without altering the model architecture or training dynamics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add dropout layers
Why it's wrong here
Reduces overfitting, not memory consumption.
- ✗
Use a smaller learning rate
Why it's wrong here
Affects convergence, not memory usage.
- ✗
Use gradient clipping
Why it's wrong here
Prevents gradient explosion, does not reduce memory.
- ✓
Reduce the batch size
Why this is correct
Smaller batch size reduces GPU memory footprint.
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