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Question 541 of 1,755

Exploratory Data Analysis →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to create bins for transaction amount and compute the fraud rate per bin. This approach is correct because correlation coefficients like Pearson’s r only measure linear relationships, and a value of 0.02 can completely mask a strong non-linear pattern, such as fraud spiking only in very high or very low transaction amounts. By binning the skewed distribution and calculating the fraud rate within each bin, the team can visually detect non-linear relationships that a simple correlation or log transformation would miss. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that EDA for detecting non-linear relationships often requires stratification or segmentation, not just transformations or model-based importance. A common trap is assuming a log transform reveals the relationship with the target, but it only normalizes the feature’s distribution. Remember the memory tip: “Correlation is linear, binning is detective”—when the relationship might curve, bin it to observe.

MLS-C01 Exploratory Data Analysis Practice Question

This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

Question 1hardmultiple choice

Full question →

A
Conclude that 'transaction amount' is not predictive because the correlation is near zero
Why wrong: Low correlation does not rule out non-linear predictive power.
B
Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'
Why wrong: Feature importance is useful but more appropriate after feature engineering; also, sample size may not be representative.
C
Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns
Binning and examining fraud rates per bin can reveal non-linear relationships.
D
Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis
Why wrong: Log transformation changes the distribution but still only measures linear correlation.

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

✓

Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns

Option B is correct because binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because log transformation does not reveal relationship with target. Option C is wrong because correlation is already computed and can mask non-linearity. Option D is wrong because feature importance from a tree model is more appropriate after feature engineering, not during EDA.

Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Conclude that 'transaction amount' is not predictive because the correlation is near zero
Why it's wrong here
Low correlation does not rule out non-linear predictive power.
✗
Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'
Why it's wrong here
Feature importance is useful but more appropriate after feature engineering; also, sample size may not be representative.
✓
Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns
Why this is correct
Binning and examining fraud rates per bin can reveal non-linear relationships.
Related concept
CIDR notation defines the prefix length.
✗
Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis
Why it's wrong here
Log transformation changes the distribution but still only measures linear correlation.

Common exam traps

Common exam trap: usable hosts are not the same as total addresses

Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.

Detailed technical explanation

How to think about this question

Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.

KKey Concepts to Remember

CIDR notation defines the prefix length.
Block size helps identify subnet boundaries.
Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
The required host count determines the smallest suitable subnet.

TExam Day Tips

Write the block size before choosing the subnet.
Check whether the question asks for hosts, subnets or a specific address range.
Do not confuse /24, /25, /26 and /27 host counts.

Key takeaway

Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.

Real-world example

How this comes up in practice

A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

What to study next

Got this wrong? Here's your next step.

Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related MLS-C01 subnetting questions on CIDR, address ranges, and subnet selection.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Exploratory Data Analysis — This question tests Exploratory Data Analysis — CIDR notation defines the prefix length..

What is the correct answer to this question?

The correct answer is: Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns — Option B is correct because binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because log transformation does not reveal relationship with target. Option C is wrong because correlation is already computed and can mask non-linearity. Option D is wrong because feature importance from a tree model is more appropriate after feature engineering, not during EDA.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

CIDR notation defines the prefix length.

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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.