← MLS-C01·Amazon Web Services

Question 175 of 1,755

Exploratory Data Analysis →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct combination includes imputing missing values in customer_age with the mean of the non-missing values and applying a log transformation to the highly skewed transaction amount. Imputing with the mean is appropriate here because the missing rate is low (5%) and customer age is likely normally distributed, so the mean preserves the central tendency without introducing bias. The log transformation addresses the right-skewed distribution by compressing the long tail and making the feature more Gaussian-like, which is critical for algorithms like logistic regression or SVM that assume normally distributed inputs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of preprocessing for imbalanced and skewed data—a common trap is to use median imputation for age when mean suffices, or to apply log transformation to the target variable instead of the feature. Remember the mnemonic: “Log the tail, mean the missing” to pair log transforms for skewness with mean imputation for low-rate missingness.

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 building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Use SMOTE to generate synthetic samples of the minority class.
Why wrong: SMOTE addresses class imbalance, not skewness or missing values. The question specifically asks for steps to handle skewness and missing values.
B
Apply standard scaling to all numerical features.
Why wrong: Standard scaling does not reduce skewness and is not necessary for all models (e.g., tree-based models).
C
Apply log transformation to the transaction amount to reduce skewness.
Log transformation is effective for reducing right skewness and can make the distribution more Gaussian-like, which benefits many models.
D
Impute missing values in customer_age with the mean of the non-missing values.
Mean imputation is a common method for handling missing numerical data when the missing rate is low (5%).
E
Drop the transaction amount feature because of its skewness.
Why wrong: Dropping a potentially important feature like transaction amount is not advisable; transformations should be attempted first.

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

✓

Apply log transformation to the transaction amount to reduce skewness.

Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. This is a standard technique for handling right-skewed distributions without losing data.

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 SMOTE to generate synthetic samples of the minority class.
Why it's wrong here
SMOTE addresses class imbalance, not skewness or missing values. The question specifically asks for steps to handle skewness and missing values.
✗
Apply standard scaling to all numerical features.
Why it's wrong here
Standard scaling does not reduce skewness and is not necessary for all models (e.g., tree-based models).
✓
Apply log transformation to the transaction amount to reduce skewness.
Why this is correct
Log transformation is effective for reducing right skewness and can make the distribution more Gaussian-like, which benefits many models.
Related concept
Read the scenario before looking for a memorised answer.
✓
Impute missing values in customer_age with the mean of the non-missing values.
Why this is correct
Mean imputation is a common method for handling missing numerical data when the missing rate is low (5%).
Related concept
Read the scenario before looking for a memorised answer.
✗
Drop the transaction amount feature because of its skewness.
Why it's wrong here
Dropping a potentially important feature like transaction amount is not advisable; transformations should be attempted first.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse handling skewness with scaling—they may choose standard scaling (Option B) thinking it addresses skewness, but standard scaling only centers and scales the data, not corrects the shape of the distribution.

Detailed technical explanation

How to think about this question

Log transformation is effective for right-skewed data because it maps multiplicative relationships to additive ones, making the distribution more symmetric and often closer to normal, which benefits algorithms that assume normally distributed features (e.g., linear discriminant analysis). For missing values in customer_age, mean imputation is a simple and fast approach, but it can reduce variance and introduce bias if the missingness is not completely random; however, with only 5% missing, it is a reasonable baseline choice before more complex imputation methods like KNN or MICE.

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.

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: Apply log transformation to the transaction amount to reduce skewness. — Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. This is a standard technique for handling right-skewed distributions without losing data.

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.