This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 training a neural network for image classification. The training loss is decreasing steadily, but the validation loss starts increasing after a few epochs. What is the MOST likely cause?
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.
The learning rate is too high
Why wrong: High learning rate would cause training loss to fluctuate or not converge.
The gradients are vanishing
Why wrong: Vanishing gradients cause training loss to plateau.
The model is underfitting
Why wrong: Underfitting would show high training loss.
The model is overfitting to the training data
Overfitting causes validation loss to increase.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The model is overfitting to the training data
The correct answer is D because the validation loss increasing while the training loss continues to decrease is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.
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.
The learning rate is too high
Why it's wrong here
High learning rate would cause training loss to fluctuate or not converge.
The gradients are vanishing
Why it's wrong here
Vanishing gradients cause training loss to plateau.
The model is underfitting
Why it's wrong here
Underfitting would show high training loss.
The model is overfitting to the training data
Why this is correct
Overfitting causes validation loss to increase.
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 distinction between overfitting and underfitting by describing a scenario where training loss decreases but validation loss increases, and the trap is that candidates may mistakenly attribute this to a high learning rate or vanishing gradients instead of recognizing it as the hallmark of overfitting.
Trap categories for this question
Command / output trap
Underfitting would show high training loss.
Detailed technical explanation
Overfitting occurs when the model's capacity (e.g., number of parameters) exceeds what is necessary for the underlying data distribution, causing it to fit idiosyncrasies in the training set. Techniques like dropout, L2 regularization, early stopping, or data augmentation are commonly used to mitigate overfitting by introducing noise or penalizing large weights. In practice, monitoring the gap between training and validation loss is a standard diagnostic for determining when to stop training.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: The model is overfitting to the training data — The correct answer is D because the validation loss increasing while the training loss continues to decrease is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.
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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