MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The scientist wants to reduce training time without changing the algorithm or the hardware. Which action is most likely to help?
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
✓
Increase the batch size used during training.
Increasing the batch size allows the model to process more samples per gradient update, reducing the number of iterations per epoch and thus speeding up training. This is a common technique to reduce training time without changing the algorithm or hardware. While it may affect convergence, it is the most direct way among the options to shorten training time. Adding regularization (B) introduces extra computation and does not reduce time. Data augmentation (C) increases the dataset size, which increases training time. Reducing epochs (D) also reduces training time, but it decreases the number of times the model sees the data, which can significantly harm model performance, making it less desirable than increasing batch size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the batch size used during training.
Why this is correct
Increasing the batch size allows the model to process more samples per gradient update, reducing the number of iterations per epoch and thus speeding up training. This is a common technique to reduce training time without changing the algorithm or hardware.
- ✗
Add regularization to the loss function.
Why it's wrong here
Adding regularization (e.g., L1/L2 penalties) introduces extra computation and does not reduce training time; it may even increase it slightly. It is used to prevent overfitting, not to speed up training.
- ✗
Use data augmentation to increase the dataset size.
Why it's wrong here
Data augmentation increases the dataset size by creating modified copies of existing data, which increases the number of samples to train on, thereby increasing training time.
- ✗
Reduce the number of training epochs.
Why it's wrong here
Reducing the number of epochs also reduces training time, but it decreases the number of times the model sees the data, which can significantly harm model performance (underfitting). While it would reduce time, it is less desirable than increasing batch size because it directly sacrifices learning.
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