The correct actions are to check for missing values in each column and decide on an imputation strategy, and to check for class imbalance in the target variable. These two steps are foundational to exploratory data analysis because missing data can bias model training if not handled properly, while class imbalance—such as a skewed ratio of churned to non-churned customers—can lead a model to predict only the majority class, rendering it useless. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of the initial data preparation phase, often appearing in scenario-based questions where distractors like PCA or one-hot encoding are listed as EDA steps. A common trap is confusing dimensionality reduction or feature engineering with basic data inspection. Remember the mnemonic “MI” for Missing values and Imbalance—these are the two checks you must perform before any modeling or transformation.
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 exploring a dataset containing customer transaction records. The target variable is 'churn' (1 = churned, 0 = not churned). Which TWO actions should the scientist take to understand the data distribution and prepare for modeling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Plot the frequency of the target variable to check for class imbalance.
Visualizing class imbalance and identifying missing values are fundamental EDA steps. Option B (PCA) is for dimensionality reduction, not initial EDA. Option D (one-hot encoding) is for categorical variables, but not an EDA action. Option E (gradient boosting) is modeling, not EDA.
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.
✗
Apply Principal Component Analysis (PCA) to reduce dimensionality.
Why it's wrong here
PCA is a modeling step, not EDA.
✗
Train a gradient boosting model to identify important features.
Why it's wrong here
Model training comes after EDA.
✓
Plot the frequency of the target variable to check for class imbalance.
Why this is correct
Essential to detect imbalance.
Related concept
Read the scenario before looking for a memorised answer.
✓
Check for missing values in each column and decide on an imputation strategy.
Why this is correct
Missing data analysis is critical in EDA.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert categorical variables into one-hot encoded vectors.
Why it's wrong here
Feature engineering, not EDA.
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: Plot the frequency of the target variable to check for class imbalance. — Visualizing class imbalance and identifying missing values are fundamental EDA steps. Option B (PCA) is for dimensionality reduction, not initial EDA. Option D (one-hot encoding) is for categorical variables, but not an EDA action. Option E (gradient boosting) is modeling, not EDA.
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.
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 working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?
hard
A.Calculating the vocabulary size
B.Creating a word cloud
C.Removing stop words
✓ D.Checking the distribution of sentiment labels
Why D: Checking for class imbalance in sentiment labels is critical because it can bias the model. Option A is wrong because stop word removal is part of preprocessing, not EDA. Option B is wrong because word clouds are for visualization, not a critical step. Option D is wrong because vocabulary size is not a primary concern at this stage.
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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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