MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single GPU instance and is taking too long. The scientist wants to reduce training time without sacrificing model accuracy. The dataset contains 100,000 images of size 256x256. Which change would most effectively reduce training time?
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 smaller image size (e.g., 128x128)
Reducing image resolution (e.g., to 128x128) significantly reduces the number of pixels and thus the computational cost per epoch, often with minimal impact on accuracy for many tasks. Using a smaller batch size increases the number of iterations but can actually slow down training. Distributed training with multiple GPUs would reduce time but the question asks for a change that does not sacrifice accuracy; distributed training can sometimes affect convergence but is generally safe. However, reducing resolution is a direct and effective method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch sizes can increase training time due to more iterations.
- ✓
Use a smaller image size (e.g., 128x128)
Why this is correct
Fewer pixels mean faster forward/backward passes, significantly reducing training time.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate can cause instability and may not converge, potentially sacrificing accuracy.
- ✗
Switch to a distributed training setup with multiple GPUs
Why it's wrong here
Distributed training can reduce time but may require code changes and could affect accuracy if not tuned properly.
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