The answer is missing values in features, multicollinearity, and class imbalance. Missing values are a fundamental issue detected during exploratory data analysis because they can bias statistical summaries and break many machine learning algorithms that require complete data. Multicollinearity, identified through correlation matrices or variance inflation factor calculations, occurs when two or more features are highly correlated, introducing redundant information that destabilizes linear models and inflates coefficient standard errors. Class imbalance, where one target class is significantly underrepresented, is another common issue spotted in EDA through frequency distributions or bar plots, and it can cause models to become biased toward the majority class. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between data quality issues that EDA can uncover versus modeling problems that arise later. A common trap is confusing multicollinearity with feature importance or overfitting, but remember: EDA is about inspecting the raw data, not the trained model. Memory tip: think of the three M’s — Missing, Multicollinearity, and Minority class imbalance.
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.
Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Multicollinearity between features
Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.
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.
✓
Multicollinearity between features
Why this is correct
High correlation between features can be detected via correlation matrix.
Related concept
Read the scenario before looking for a memorised answer.
✗
High latency in API endpoints
Why it's wrong here
Latency is a performance metric, not a data characteristic.
✗
Gradient vanishing in neural networks
Why it's wrong here
Gradient vanishing is a training issue, not a data issue.
✓
Class imbalance in the target variable
Why this is correct
Imbalanced classes are identified by examining target distribution.
Related concept
Read the scenario before looking for a memorised answer.
✓
Missing values in features
Why this is correct
Missing data is a common data quality issue detected during EDA.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the boundary between data-level issues (EDA) and model training issues, so candidates mistakenly select gradient vanishing (a deep learning optimization problem) or API latency (an operational concern) as EDA findings.
Detailed technical explanation
How to think about this question
Multicollinearity is typically detected using a VIF score above 5 or 10, or by inspecting pairwise Pearson correlation coefficients exceeding 0.8. In real-world scenarios, features like 'years of experience' and 'age' often exhibit multicollinearity, leading to unreliable coefficient estimates in regression models and making feature selection or dimensionality reduction (e.g., PCA) necessary during EDA.
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.
TExam Day Tips
→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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Multicollinearity between features — Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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