MLS-C01 Modeling Practice Question
A machine learning team is using Amazon SageMaker to train a linear regression model. The team notices that the training loss decreases rapidly initially but then plateaus at a high value. What is the MOST likely cause?
⚠ Common exam trap
The trap here is that candidates often associate a plateau in loss with a learning rate that is too low (underfitting), but the rapid initial decrease followed by a high plateau is a classic sign of a learning rate that is too high, causing divergence or oscillation.
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
✓
The learning rate is set too high
A learning rate set too high causes the optimizer to take excessively large steps, overshooting the minimum of the loss function. This results in rapid initial decrease as the model makes large corrections, but then the loss plateaus at a high value because the parameters oscillate around the optimum without converging. In SageMaker's linear regression (typically using stochastic gradient descent), a high learning rate prevents fine-grained convergence, leading to a high 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.
- ✗
The model uses batch normalization
Why it's wrong here
Batch normalization typically improves convergence, not causes plateaus.
- ✗
The learning rate is set too low
Why it's wrong here
A low learning rate leads to slow convergence, not a rapid initial decrease followed by a plateau.
- ✗
The model is over-regularized with L2 regularization
Why it's wrong here
L2 regularization penalizes large weights but does not typically cause a plateau in loss.
- ✓
The learning rate is set too high
Why this is correct
A high learning rate can cause the loss to fluctuate or plateau after an initial drop.
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