MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?
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 pandas .isnull().sum() in a SageMaker notebook
Using pandas .isnull().sum() in a SageMaker notebook is the most appropriate method because it directly provides the count (and thus the percentage when divided by total rows) of missing values per column, which is a standard exploratory data analysis technique. Option A is incorrect because Amazon S3 Select is used for filtering and retrieving subsets of data from S3 objects, not for computing missing values. Option B is incorrect because while Amazon Athena can run SQL queries like SELECT COUNT(*), it is less direct for per-column missing value analysis and requires a schema. Option C is incorrect because Amazon QuickSight is a visualization tool, not designed for programmatic missing value detection. Option D is incorrect because AWS Glue Crawler discovers schema and partitions, not missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon S3 Select to query missing values
Why it's wrong here
S3 Select is for retrieving subsets of data, not for computing missing percentages.
- ✗
Use Amazon Athena to run a SELECT COUNT(*) query
Why it's wrong here
Athena is more suited for SQL-based analysis but requires more setup.
- ✗
Use Amazon QuickSight to create a missing value dashboard
Why it's wrong here
QuickSight can visualize missing data but is not the most direct method for initial EDA.
- ✗
Use AWS Glue Crawler to detect missing values
Why it's wrong here
Glue Crawler infers schema and partitions, not missing values.
- ✓
Use pandas .isnull().sum() in a SageMaker notebook
Why this is correct
This is a direct and efficient way to count missing values per column.
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 |
Go deeper
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