MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. Which action would MOST likely reduce training time without sacrificing model accuracy?
⚠ Common exam trap
Watch out — candidates often confuse reducing training time with reducing computational load (e.g., smaller instance or fewer epochs), but the question specifically requires maintaining accuracy, which distributed parallelism achieves by leveraging more hardware rather than cutting corners in the training process.
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
✓
Enable data parallelism across multiple GPUs
Enabling data parallelism across multiple GPUs distributes the training workload across several devices, allowing larger batch sizes and faster gradient computation per epoch. Amazon SageMaker's distributed training libraries (e.g., SageMaker Data Parallelism) use all-reduce algorithms to synchronize gradients efficiently, which reduces wall-clock training time without altering the model architecture or loss function, thus preserving accuracy.
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 smaller instance type for training
Why it's wrong here
Smaller instances have less compute power, leading to longer training times.
- ✗
Implement early stopping with a low patience value
Why it's wrong here
Early stopping can prevent overfitting but may end training too early, reducing accuracy.
- ✓
Enable data parallelism across multiple GPUs
Why this is correct
Data parallelism distributes data across GPUs, reducing training time while preserving accuracy.
- ✗
Reduce the number of epochs by half
Why it's wrong here
Reducing epochs can lower training time but often at the cost of accuracy.
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