The answer is overfitting, as the model’s high training accuracy of 99% paired with a significantly lower validation accuracy of 82% reveals it has memorized the training data rather than learning generalizable patterns. This gap is the classic hallmark of overfitting, where the model captures noise and specific quirks of the training set but fails to perform on unseen validation data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose model performance issues by comparing training and validation metrics, often appearing in questions about bias-variance tradeoff or regularization techniques. A common trap is to assume data imbalance or leakage, but here the balanced 10,000 positive and 10,000 negative reviews rule out class imbalance, and no evidence points to leakage. Remember the memory tip: “High train, low valid—overfit is valid.”
MLS-C01 Modeling Practice Question
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 company is building a sentiment analysis model for customer reviews. The dataset includes 10,000 positive and 10,000 negative reviews. The data scientist splits the data into 70% training, 15% validation, and 15% test sets. After training, the model achieves 99% accuracy on training set but only 82% on validation set. What is the most likely 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.
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
Option B is correct because a large gap between training and validation accuracy indicates overfitting. Option A is wrong because the dataset is balanced. Option C is wrong because underfitting would show low training accuracy. Option D is wrong because data leakage is not indicated by accuracy gap alone.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
There is data leakage from validation to training
Why it's wrong here
Data leakage would likely cause high validation accuracy, not a gap.
✗
The dataset is imbalanced
Why it's wrong here
The dataset has equal positive and negative examples, so it is balanced.
✗
The model is underfitting
Why it's wrong here
Underfitting would show low accuracy on both training and validation sets.
✓
The model is overfitting
Why this is correct
High training accuracy with significantly lower validation accuracy is a classic sign of overfitting.
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
Common exam trap: answer the scenario, not the keyword
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
Command / output trap
Underfitting would show low accuracy on both training and validation sets.
Detailed technical explanation
How to think about this question
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.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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 — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model is overfitting — Option B is correct because a large gap between training and validation accuracy indicates overfitting. Option A is wrong because the dataset is balanced. Option C is wrong because underfitting would show low training accuracy. Option D is wrong because data leakage is not indicated by accuracy gap alone.
What should I do if I get this MLS-C01 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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