← MLS-C01·Amazon Web Services

Question 368 of 1,755

Exploratory Data Analysis →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to bin the multimodal feature 'tenure' into categorical groups and remove one of the highly correlated features, such as 'total_charges', to handle multicollinearity. Binning multimodal features like 'tenure' transforms a continuous variable with multiple peaks into discrete segments, allowing a linear model like logistic regression to capture non-linear relationships without assuming a constant slope across all values. Removing one of the correlated features addresses multicollinearity, which inflates coefficient standard errors and destabilizes the model when two predictors, like 'monthly_charges' and 'total_charges', share nearly identical information. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of feature engineering for linear models and the trade-offs between interpretability and model assumptions. A common trap is keeping both correlated features, thinking more data is better, or leaving 'tenure' continuous, which forces the model to miss the churn spikes at contract milestones. Memory tip: for multimodal features, think “bin the peaks”; for multicollinearity, “one of the pair must go.”

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 machine learning engineer is working on a customer churn prediction project. The dataset contains 100,000 records with 15 features, including customer demographics, account information, and usage patterns. The target variable 'churned' is binary with 15% positive examples. During EDA, the engineer notices that the feature 'tenure' (number of months the customer has been with the company) has a multimodal distribution with peaks at 1, 12, 24, and 36 months. Also, the feature 'monthly_charges' has a strong positive correlation with 'total_charges' (correlation coefficient = 0.95). The engineer wants to build a logistic regression model. Which preprocessing steps should the engineer take to address these issues? (Select TWO.)

Question 1mediummultiple choice

Read the full NAT/PAT explanation →

A
Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.
Binning can effectively capture the peaks in the distribution and model the non-linear effect of tenure on churn.
B
Remove one of the correlated features, such as 'total_charges', to reduce multicollinearity.
Highly correlated features can cause instability in logistic regression coefficient estimates; removing one helps.
C
Apply log transformation to the 'tenure' feature to make it unimodal.
Why wrong: Log transformation does not make a multimodal distribution unimodal; it only compresses the scale.
D
Create polynomial features up to degree 3 for 'tenure' to capture non-linearity.
Why wrong: Polynomial features can introduce multicollinearity and are not the best approach for multimodal distributions; binning is more interpretable.
E
Standardize all numerical features to have mean 0 and variance 1.
Why wrong: Standardization is good for logistic regression with regularization, but it does not address the specific issues of multicollinearity and multimodality mentioned.

Full breakdown with real-world context →

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

✓

Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.

Option A is correct because binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption.

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.

✓
Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.
Why this is correct
Binning can effectively capture the peaks in the distribution and model the non-linear effect of tenure on churn.
Related concept
Read the scenario before looking for a memorised answer.
✓
Remove one of the correlated features, such as 'total_charges', to reduce multicollinearity.
Why this is correct
Highly correlated features can cause instability in logistic regression coefficient estimates; removing one helps.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply log transformation to the 'tenure' feature to make it unimodal.
Why it's wrong here
Log transformation does not make a multimodal distribution unimodal; it only compresses the scale.
✗
Create polynomial features up to degree 3 for 'tenure' to capture non-linearity.
Why it's wrong here
Polynomial features can introduce multicollinearity and are not the best approach for multimodal distributions; binning is more interpretable.
✗
Standardize all numerical features to have mean 0 and variance 1.
Why it's wrong here
Standardization is good for logistic regression with regularization, but it does not address the specific issues of multicollinearity and multimodality mentioned.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the misconception that standardizing or transforming features alone can fix non-linear relationships or multicollinearity, but these steps do not address the root cause of multimodal distributions or high feature correlation in linear models like logistic regression.

Detailed technical explanation

How to think about this question

Multimodal distributions in features like 'tenure' often reflect business cycles (e.g., annual contract renewals), and logistic regression's linear decision boundary cannot capture such patterns without feature engineering. Multicollinearity (correlation coefficient = 0.95) inflates the variance of coefficient estimates, making the model unstable and interpretations unreliable; removing one correlated feature (e.g., 'total_charges') is a standard remedy because the remaining feature carries nearly the same information. In practice, binning should be done with domain knowledge (e.g., contract lengths) to avoid information loss, and correlation thresholds above 0.8 are commonly used to trigger feature removal.

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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Related MLS-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Engineering practice questions

Practise MLS-C01 questions linked to Data Engineering.

Machine Learning Implementation and Operations practice questions

Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.

Modeling practice questions

Practise MLS-C01 questions linked to Modeling.

Exploratory Data Analysis practice questions

Practise MLS-C01 questions linked to Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Practise MLS-C01 questions linked to MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Practise MLS-C01 questions linked to MLS-C01 troubleshooting.

Practice this exam

Start a free MLS-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

MLS-C01 practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this MLS-C01 question test?

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: Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship. — Option A is correct because binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption.

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 →

How Courseiva writes practice questions · Editorial policy

Keep practising

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.

Loading comments…

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.