Question 1,239 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear learner model for regression. After reviewing the training logs, the data scientist notices that the loss is not decreasing and remains high. The learning rate is set to 0.01. The data is normalized. What should the data scientist do to improve convergence?
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
✓
Try different learning rates, such as 0.001 or 0.1.
The loss is not decreasing, which often indicates an inappropriate learning rate. Trying different learning rates (e.g., 0.001 or 0.1) can help find a rate that allows the model to converge. Option A is incorrect because the data is already normalized; normalizing again would have no effect. Option B is incorrect because reducing the mini-batch size introduces more stochasticity but does not directly address learning rate issues. Option D is incorrect because increasing epochs does not help if the learning rate prevents convergence; the model may continue to oscillate or plateau.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize the data again.
Why it's wrong here
Data is already normalized.
- ✗
Reduce the mini-batch size.
Why it's wrong here
Reducing batch size may introduce noise but is less likely to be the primary fix.
- ✓
Try different learning rates, such as 0.001 or 0.1.
Why this is correct
Tuning the learning rate is a common first step to improve convergence.
- ✗
Increase the number of epochs.
Why it's wrong here
More epochs may not help if the loss is stuck due to suboptimal learning rate.
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Last reviewed: Jun 20, 2026
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