Quick Answer
The answer is to reduce the learning rate to 0.01. This resolves the NaN loss error because an excessively high learning rate causes gradient descent updates to overshoot the optimal weight values, leading to divergence and numerical instability where the loss function produces undefined values. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of gradient descent dynamics and hyperparameter tuning for built-in algorithms like Linear Learner. A common trap is to increase regularization or batch size, but the root cause is almost always an aggressive learning rate that destabilizes training. Remember the memory tip: "High rate, NaN fate; low rate, steady state."
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Increase the learning rate to 0.5
Why wrong: Higher learning rate worsens exploding gradients.
Increase mini_batch_size to 2000
Why wrong: Batch size may not fix NaN directly.
Decrease epochs to 5
Why wrong: Fewer epochs may not fix NaN.
Reduce learning_rate to 0.01
Lower learning rate helps convergence.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
The Linear Learner algorithm uses stochastic gradient descent (SGD) with an adaptive learning rate schedule by default; when the initial learning rate is too high, the loss can explode to infinity (NaN) due to gradient explosion. Under the hood, the algorithm computes the loss as the mean squared error or cross-entropy, and if the weights become NaN, all subsequent computations propagate that value. In practice, this error often surfaces when training on unscaled features or with a learning rate that exceeds the optimal range for the dataset's curvature.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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