MLS-C01 Exploratory Data Analysis Practice Question
A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?
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
✓
Analyze the missing data pattern using visualizations and summary statistics
During EDA, the first step is to understand the pattern and extent of missing data using visualizations and summary statistics. This helps determine whether missingness is random or systematic, and guides the choice of imputation or deletion. Option A is wrong because imputing with the mean without understanding the missing mechanism can introduce bias. Option C is wrong because dropping rows may discard valuable data and reduce sample size unnecessarily. Option D is wrong because using SageMaker Data Wrangler is a specific tool and may not be necessary; EDA focuses on understanding data, not automated imputation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute missing values with the mean of each column
Why it's wrong here
Mean imputation without understanding may distort distributions.
- ✓
Analyze the missing data pattern using visualizations and summary statistics
Why this is correct
Understanding the missing data pattern is crucial before deciding on imputation or deletion.
- ✗
Drop all rows with missing values to ensure data quality
Why it's wrong here
Dropping rows may discard valuable information and reduce sample size.
- ✗
Use Amazon SageMaker Data Wrangler to automatically impute missing values
Why it's wrong here
Automated imputation may not be appropriate without exploratory analysis.
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