MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?
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
✓
Use multiple imputation to generate several plausible values and combine results
Multiple imputation provides unbiased estimates under MAR by accounting for uncertainty and preserving relationships between variables. Option A is wrong because mean imputation reduces variance, distorts distributions, and can bias estimates. Option B is wrong because listwise deletion reduces sample size and can introduce bias if missingness is related to other variables. Option C is wrong because median imputation is a single imputation method that does not account for the uncertainty due to missing data and may still introduce bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace missing values with the mean (45)
Why it's wrong here
Mean imputation distorts the distribution and can bias correlations.
- ✗
Remove rows with missing 'age' values
Why it's wrong here
Listwise deletion can introduce bias if the missingness is related to other variables.
- ✗
Replace missing values with the median
Why it's wrong here
Median imputation is robust but still a single imputation method that underestimates variability.
- ✓
Use multiple imputation to generate several plausible values and combine results
Why this is correct
Multiple imputation preserves the natural variability and provides valid statistical inferences under MAR.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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