MLS-C01 Modeling Practice Question
During training of a SageMaker built-in object detection algorithm, the loss is not decreasing after several epochs. Which troubleshooting step should be taken first?
⚠ Common exam trap
It's easy for candidates to assume increasing the number of epochs (Option D) will always reduce loss, but they fail to recognize that a plateauing loss is typically a sign of a hyperparameter issue like learning rate, not insufficient 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
✓
Check whether the learning rate is appropriate
When the loss is not decreasing during training of a SageMaker built-in object detection algorithm, the most common cause is an inappropriate learning rate. A learning rate that is too high can cause the loss to oscillate or diverge, while one that is too low can cause the loss to plateau. Checking and adjusting the learning rate is the first troubleshooting step because it directly controls the step size of gradient updates and is a fundamental hyperparameter in optimization.
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 mini-batch size
Why it's wrong here
Increasing batch size may not help if loss is not decreasing; it could even slow convergence.
- ✗
Add more classes to the dataset
Why it's wrong here
Adding classes makes the problem harder and does not troubleshoot training issues.
- ✓
Check whether the learning rate is appropriate
Why this is correct
Learning rate is a critical hyperparameter; incorrect value often causes loss not to decrease.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs won't help if the model is not learning due to other issues.
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