MLS-C01 Modeling Practice Question
A media company uses SageMaker to train a neural network for content recommendation. The model uses embeddings for users and items. Training is slow and they want to reduce time. The dataset has 10 million users and 1 million items. They have a cluster of 8 p3.16xlarge instances. Which strategy is most likely to reduce training time?
⚠ Common exam trap
Watch out — candidates often default to data parallelism as the standard approach for distributed training, failing to recognize that when the model itself (especially embedding layers) exceeds GPU memory, model parallelism is required to scale out effectively.
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 SageMaker's model parallelism to split the embedding layers across GPUs
SageMaker's model parallelism splits the embedding layers across GPUs, which is essential when the embedding table is too large to fit into the memory of a single GPU. With 10 million users and 1 million items, even with a modest embedding dimension of 256, the embedding layer alone can exceed 10 GB, causing memory bottlenecks that slow training. Model parallelism distributes these large parameters across multiple GPUs, reducing per-GPU memory pressure and enabling larger batch sizes, which directly reduces training time.
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 data parallelism to replicate the model on each GPU and synchronize gradients
Why it's wrong here
Data parallelism may not help if the model is memory-bound due to embeddings.
- ✗
Reduce the embedding dimension from 256 to 64
Why it's wrong here
This reduces accuracy and may not be acceptable.
- ✓
Use SageMaker's model parallelism to split the embedding layers across GPUs
Why this is correct
Model parallelism distributes large embedding tables across devices, reducing memory and enabling larger batches.
- ✗
Use a smaller batch size to fit on each GPU
Why it's wrong here
Smaller batch sizes increase training time.
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