MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?
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
✓
Visualize the missingness using a heatmap or bar chart.
Visualizing missingness with a heatmap or bar chart (using libraries like missingno) reveals patterns such as MCAR, MAR, or MNAR. Option A (correlation matrix) does not directly show missingness patterns. Option B (dropping rows) may remove valuable data and assumes MCAR. Option C (mean imputation) also assumes MCAR and can bias results if missingness is not random.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute the correlation matrix of the features with missing values.
Why it's wrong here
Correlation does not reveal missingness patterns.
- ✗
Drop all rows with any missing values.
Why it's wrong here
This reduces data and may introduce bias.
- ✗
Impute all missing values with the mean of each column.
Why it's wrong here
Mean imputation assumes data is MCAR, which may not hold.
- ✓
Visualize the missingness using a heatmap or bar chart.
Why this is correct
Visualization helps identify patterns like monotonic or random missingness.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.