Quick Answer
The answer is overfitting, as a large AUC gap between training and validation sets is the classic signature of this problem. When a model achieves an AUC of 0.99 on training data but only 0.72 on validation data, it has memorized noise and patterns specific to the training set rather than learning generalizable features, causing it to fail on unseen data. This concept is a frequent trap on the AWS Certified Machine Learning Specialty MLS-C01 exam, where you must distinguish overfitting from underfitting (poor performance on both sets), data leakage (inflated metrics on both), or class imbalance (affecting both sets similarly). The key to overfitting detection using AUC gap lies in recognizing that a high training AUC paired with a significantly lower validation AUC always points to excessive model complexity or insufficient regularization. Memory tip: think of the “gap” as a red flag—if the gap is wide, the model has “gone too far” in fitting the training data.
This MLS-C01 practice question tests your understanding of modeling. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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.
During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following 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.
Class imbalance in the training set.
Why wrong: Class imbalance affects both sets similarly.
Underfitting.
Why wrong: Underfitting would yield low AUC on both sets.
Overfitting.
Overfitting results in high training but lower validation AUC.
Data leakage from validation to training.
Why wrong: Data leakage would cause both metrics to be high.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Overfitting.
Option B is correct because a large gap between training and validation performance indicates overfitting. Option A is wrong because underfitting would show poor performance on both. Option C is wrong because data leakage would inflate both metrics. Option D is wrong because class imbalance would affect both sets similarly.
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.
Class imbalance in the training set.
Why it's wrong here
Class imbalance affects both sets similarly.
Underfitting.
Why it's wrong here
Underfitting would yield low AUC on both sets.
Overfitting.
Why this is correct
Overfitting results in high training but lower validation AUC.
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.
Data leakage from validation to training.
Why it's wrong here
Data leakage would cause both metrics to be high.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Class imbalance affects both sets similarly.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — study guide chapter
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Overfitting. — Option B is correct because a large gap between training and validation performance indicates overfitting. Option A is wrong because underfitting would show poor performance on both. Option C is wrong because data leakage would inflate both metrics. Option D is wrong because class imbalance would affect both sets similarly.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 20, 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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