MLS-C01 Modeling Practice Question
A company is training a deep learning model on a large dataset using Amazon SageMaker. The training script uses TensorFlow and requires GPUs. The training job is failing with an out-of-memory error. Which configuration change should be made to resolve this issue?
⚠ Common exam trap
A common mix-up: candidates confuse horizontal scaling (adding instances) with vertical scaling (increasing instance size), assuming that more instances will magically fix a per-GPU memory limit, when in fact distributed training requires the model to fit on each GPU unless model parallelism is explicitly implemented.
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 a larger instance type with more GPU memory.
The training job is failing with an out-of-memory error, which indicates that the model or batch size exceeds the GPU memory capacity of the current instance. Using a larger instance type with more GPU memory directly addresses this by providing additional VRAM, allowing the model to fit in memory and the training to proceed without failure.
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 a larger instance type with more GPU memory.
Why this is correct
Larger instance types have more GPU memory, resolving the OOM error.
- ✗
Increase the number of instances in the training job.
Why it's wrong here
Adding instances does not increase per-instance memory; it only distributes the workload.
- ✗
Switch to using spot instances to reduce cost.
Why it's wrong here
Spot instances do not provide more memory and may be interrupted.
- ✗
Enable distributed training across multiple instances.
Why it's wrong here
Distributed training requires code changes and does not increase per-instance memory.
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