Quick Answer
The answer is that SMOTE was applied before splitting the data into training and test sets, causing data leakage. When SMOTE generates synthetic samples using the entire dataset, it creates artificial points that are influenced by information from what should be the unseen test set. This leakage artificially inflates the training data’s diversity, leading to a model that appears highly accurate on the test set but fails to generalize to real-world positive cases, hence the dismal 5% recall despite 99% accuracy. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the proper preprocessing pipeline order: always split first, then apply SMOTE only to the training fold. A common trap is assuming high accuracy means a good model, but imbalanced data demands you check recall or precision instead. Memory tip: “Split before you SMOTE” — keep your test set pristine to avoid synthetic contamination.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 working on a binary classification problem with a highly imbalanced dataset (1% positive class). They have applied oversampling using SMOTE and trained a logistic regression model. The model achieves 99% accuracy on the test set, but the recall for the positive class is only 5%. 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.
SMOTE was applied before splitting the data into training and test sets
Why D is correct
The model is overfitting due to lack of regularization
Why wrong: Why C is wrong
Accuracy is not a suitable metric for imbalanced data
Why wrong: Why B is wrong
Logistic regression is inappropriate for imbalanced datasets
Why wrong: Why A is wrong
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SMOTE was applied before splitting the data into training and test sets
Option D is correct because if SMOTE was applied before splitting, synthetic samples leak information from the test set into the training set, leading to overoptimistic accuracy but poor generalization. Option A is wrong because logistic regression can handle balanced data, though it may not capture complex patterns. Option B is wrong because accuracy is a poor metric for imbalanced data, but the low recall indicates a problem beyond metric choice. Option C is wrong because while L2 regularization might help, it would not cause such a discrepancy between accuracy and recall.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
SMOTE was applied before splitting the data into training and test sets
Why this is correct
Why D is correct
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
The model is overfitting due to lack of regularization
Why it's wrong here
Why C is wrong
Accuracy is not a suitable metric for imbalanced data
Why it's wrong here
Why B is wrong
Logistic regression is inappropriate for imbalanced datasets
Why it's wrong here
Why A is wrong
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
The correct answer is: SMOTE was applied before splitting the data into training and test sets — Option D is correct because if SMOTE was applied before splitting, synthetic samples leak information from the test set into the training set, leading to overoptimistic accuracy but poor generalization. Option A is wrong because logistic regression can handle balanced data, though it may not capture complex patterns. Option B is wrong because accuracy is a poor metric for imbalanced data, but the low recall indicates a problem beyond metric choice. Option C is wrong because while L2 regularization might help, it would not cause such a discrepancy between accuracy and recall.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Static NAT maps one inside address to one outside address.
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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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