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.
A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features and 100,000 observations. The model's training loss is decreasing, but the validation loss starts increasing after a few epochs. Which step should the data scientist take first to address this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Add more features to the model
Why wrong: Adding features increases complexity and may worsen overfitting.
Reduce the learning rate
Reducing the learning rate can help the model converge more stably and reduce overfitting.
Increase the batch size
Why wrong: Increasing batch size can reduce noise but is not the most direct step for overfitting.
Increase the number of epochs
Why wrong: More epochs would likely increase overfitting.
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 the learning rate
The increasing validation loss while training loss decreases is a classic sign of overfitting. Reducing the learning rate (Option B) is the first step to stabilize training by allowing the optimizer to take smaller, more controlled steps, which can help the model converge to a better local minimum and reduce validation loss. In SageMaker, this is typically adjusted via the `learning_rate` hyperparameter in the estimator.
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.
Add more features to the model
Why it's wrong here
Adding features increases complexity and may worsen overfitting.
Reduce the learning rate
Why this is correct
Reducing the learning rate can help the model converge more stably and reduce overfitting.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase the batch size
Why it's wrong here
Increasing batch size can reduce noise but is not the most direct step for overfitting.
Increase the number of epochs
Why it's wrong here
More epochs would likely increase overfitting.
Common exam traps
The trap here is that candidates often confuse overfitting with underfitting and incorrectly choose to add more features or increase epochs, not realizing that the validation loss increase is a direct sign of overfitting that requires reducing model capacity or learning rate.
Detailed technical explanation
Overfitting occurs when the model learns noise in the training data, often due to a learning rate that is too high, causing the optimizer to oscillate around or overshoot a good minimum. Reducing the learning rate allows the model to fine-tune weights more precisely, often in conjunction with early stopping or regularization. In SageMaker's built-in Linear Learner algorithm, the `learning_rate` hyperparameter controls the step size of stochastic gradient descent (SGD), and a common practice is to reduce it by a factor of 10 when validation loss plateaus or increases.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the learning rate — The increasing validation loss while training loss decreases is a classic sign of overfitting. Reducing the learning rate (Option B) is the first step to stabilize training by allowing the optimizer to take smaller, more controlled steps, which can help the model converge to a better local minimum and reduce validation loss. In SageMaker, this is typically adjusted via the `learning_rate` hyperparameter in the estimator.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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