- A
Generating a correlation matrix of features
Why wrong: Correlation matrix helps with multicollinearity, not class imbalance.
- B
Log transformation of skewed features
Why wrong: Log transformation addresses feature skew, not class imbalance.
- C
Checking for class imbalance in the target variable
Class imbalance can lead to high training accuracy but poor generalization.
- D
Creating a heatmap of missing values
Why wrong: Missing value heatmaps show missing data, not class imbalance.
Quick Answer
The answer is checking for class imbalance in the target variable. This is the correct exploratory step because class imbalance in logistic regression can produce a model that achieves 95% accuracy by simply predicting the majority class for every instance, yet it fails on the minority class when evaluated on unseen test data—a classic case of high training accuracy masking poor generalization. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that accuracy is a misleading metric for imbalanced datasets, and it often appears as a trap where candidates confuse class imbalance with feature skewness or multicollinearity. A common memory tip: if your model has suspiciously high accuracy but fails on the test set, think “majority class cheat”—always check the target variable’s class distribution first.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 runs a logistic regression and obtains a model with 95% accuracy on the training set. However, the model performs poorly on the test set. Which exploratory data analysis step should have been performed to identify this issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Checking for class imbalance in the target variable
Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A is wrong because log transformation is for skewness, not class imbalance. Option C is wrong because a correlation matrix helps with multicollinearity. Option D is wrong because missing value heatmaps show missing data patterns.
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
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Generating a correlation matrix of features
Why it's wrong here
Correlation matrix helps with multicollinearity, not class imbalance.
- ✗
Log transformation of skewed features
Why it's wrong here
Log transformation addresses feature skew, not class imbalance.
- ✓
Checking for class imbalance in the target variable
Why this is correct
Class imbalance can lead to high training accuracy but poor generalization.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Creating a heatmap of missing values
Why it's wrong here
Missing value heatmaps show missing data, not class imbalance.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
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.
Trap categories for this question
Command / output trap
Missing value heatmaps show missing data, not class imbalance.
Detailed technical explanation
How to think about this question
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.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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
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. 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
Got this wrong? Here's your next step.
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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Exploratory Data Analysis practice questions
Targeted practice on this topic area only
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Checking for class imbalance in the target variable — Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A is wrong because log transformation is for skewness, not class imbalance. Option C is wrong because a correlation matrix helps with multicollinearity. Option D is wrong because missing value heatmaps show missing data patterns.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
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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