The answer is to compute mutual information between each feature and the target, and apply Principal Component Analysis (PCA). Mutual information is the correct choice for identifying important features because it captures any non-linear relationship between a feature and the binary target, making it robust to non-normal distributions and high correlation. PCA then handles the high correlation by transforming the correlated features into uncorrelated principal components, reducing dimensionality without requiring normality. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between correlation-sensitive methods like PCA and normality-dependent techniques like Pearson correlation; a common trap is choosing Pearson correlation, which assumes linearity and normality. Remember the memory tip: “PCA for correlation, mutual info for non-linear and non-normal”—pair them to handle both challenges effectively.
MLS-C01 Exploratory Data Analysis Practice Question
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 analyzing a dataset with 100 features and 10,000 observations. The target variable is binary (0/1). Initial exploratory data analysis reveals that many features have missing values, high correlation with each other, and non-normal distributions. The data scientist wants to identify the most important features for predicting the target while reducing dimensionality. Which TWO actions should the data scientist take? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply Principal Component Analysis (PCA) to reduce dimensionality.
B is correct because Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated features into a set of linearly uncorrelated principal components, effectively handling high correlation and reducing the feature space. It does not require normality assumptions and can work with missing values after imputation, making it suitable for this dataset.
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.
✗
Use chi-squared test to rank features by p-value.
Why it's wrong here
Chi-squared test is for categorical features, not continuous.
✓
Apply Principal Component Analysis (PCA) to reduce dimensionality.
Why this is correct
PCA reduces dimensionality by creating uncorrelated components, handling multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Perform a t-test for each feature to compare means between classes.
Why it's wrong here
t-test is for two-group comparison, not suitable for ranking multiple features.
✗
Calculate Pearson correlation coefficients between features and target.
Why it's wrong here
Pearson correlation assumes linearity and normality, which are violated.
✓
Compute mutual information between each feature and the target.
Why this is correct
Mutual information captures non-linear dependencies and works with non-normal data.
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 misconception that correlation-based methods (like Pearson or chi-squared) are sufficient for feature selection in high-dimensional, non-normal data, when in fact they fail due to assumptions about linearity and distribution.
Detailed technical explanation
How to think about this question
PCA works by computing the eigenvectors of the covariance matrix of the data, projecting the original features onto orthogonal components that capture maximum variance. In practice, PCA can be sensitive to feature scaling, so standardizing features (e.g., using StandardScaler) is critical before applying PCA. A real-world scenario is in genomic data analysis, where thousands of highly correlated gene expression levels are reduced to a few principal components for downstream classification.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Apply Principal Component Analysis (PCA) to reduce dimensionality. — B is correct because Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated features into a set of linearly uncorrelated principal components, effectively handling high correlation and reducing the feature space. It does not require normality assumptions and can work with missing values after imputation, making it suitable for this dataset.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?
easy
✓ A.Apply principal component analysis (PCA) and select the top components
B.Use L1 regularization (Lasso) to shrink coefficients to zero
C.Train a random forest and remove features with low importance
D.Compute the correlation matrix and remove features with high correlation
Why A: Option B is correct because principal component analysis (PCA) is a dimensionality reduction technique that identifies the principal components capturing the most variance. Option A is wrong because correlation matrix only shows pairwise linear relationships, not importance. Option C is wrong because regularization can shrink coefficients but is not a dedicated dimensionality reduction technique. Option D is wrong because random forests can provide feature importance but are not a dimensionality reduction technique per se.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.