MLS-C01 Modeling Practice Question
Network Topology
Refer to the exhibit. A SageMaker training job using the built-in Linear Learner algorithm fails with 'Loss function returned NaN'. Which hyperparameter change is MOST likely to resolve this issue?
⚠ Common exam trap
AWS often tests the misconception that increasing the learning rate speeds up convergence, but the trap here is that a high learning rate causes divergence and NaN loss, so the correct fix is to reduce it, not increase it.
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
✓
Reduce learning_rate to 0.01
The 'Loss function returned NaN' error in SageMaker's built-in Linear Learner algorithm typically occurs when the learning rate is too high, causing gradient updates to overshoot optimal parameters and diverge. Reducing the learning rate to 0.01 stabilizes training by ensuring smaller, more controlled weight updates, preventing numerical instability that leads to NaN loss.
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 learning rate to 0.5
Why it's wrong here
Higher learning rate worsens exploding gradients.
- ✗
Increase mini_batch_size to 2000
Why it's wrong here
Batch size may not fix NaN directly.
- ✗
Decrease epochs to 5
Why it's wrong here
Fewer epochs may not fix NaN.
- ✓
Reduce learning_rate to 0.01
Why this is correct
Lower learning rate helps convergence.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.