The answer is to train a Random Forest classifier and use the feature_importances_ attribute. This technique is correct because Random Forest inherently computes feature importance by measuring how much each feature reduces impurity (typically Gini impurity) across all decision trees in the ensemble, providing a reliable ranking of predictive power for a binary target. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of supervised feature selection versus unsupervised dimensionality reduction—a common trap is confusing PCA or t-SNE, which ignore the target variable, with methods that leverage labeled data. Remember that Random Forest feature importance is model-based and directly tied to classification performance, making it ideal for identifying which of the 50 columns most influence the binary outcome. A quick memory tip: if the target is involved, think “supervised scoring”; if not, think “unsupervised transformation.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a Random Forest classifier and use feature_importances_
Option A is correct because Random Forest feature importance is a well-known method for ranking features in classification tasks. Option B is wrong because PCA is unsupervised and does not use the target. Option C is wrong because K-means is clustering, not feature selection. Option D is wrong because t-SNE is for visualization, not feature importance.
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 t-SNE to reduce dimensionality and inspect clusters
Why it's wrong here
t-SNE is for visualization, not feature selection.
✗
Run K-means clustering and examine cluster centroids
Why it's wrong here
K-means is unsupervised and does not identify feature importance for prediction.
✓
Train a Random Forest classifier and use feature_importances_
Why this is correct
Random Forest provides feature importance scores based on impurity reduction.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply PCA and select components with highest variance
Why it's wrong here
PCA does not consider the target variable.
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.
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.
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: Train a Random Forest classifier and use feature_importances_ — Option A is correct because Random Forest feature importance is a well-known method for ranking features in classification tasks. Option B is wrong because PCA is unsupervised and does not use the target. Option C is wrong because K-means is clustering, not feature selection. Option D is wrong because t-SNE is for visualization, not feature importance.
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.
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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