MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The analysis reveals high cardinality in a categorical feature with over 1 million unique values. What is the best approach to handle this before training a model?
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 target encoding based on the mean of the target variable per category.
Target encoding (also known as mean encoding) replaces each category with the mean of the target variable for that category, effectively handling high cardinality without exploding dimensionality or imposing ordinal relationships. Option A is wrong because one-hot encoding on a feature with 1 million unique values would create over 1 million columns, making the dataset sparse and computationally expensive. Option B is wrong because label encoding assigns arbitrary integers, which may introduce unintended ordinal relationships and mislead the model. Option C is wrong because dropping the feature could discard valuable predictive information.
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 one-hot encoding.
Why it's wrong here
One-hot encoding produces too many features.
- ✗
Use label encoding to convert categories to integers.
Why it's wrong here
Label encoding can imply ordinality.
- ✗
Drop the high-cardinality feature.
Why it's wrong here
Dropping may discard valuable information.
- ✓
Use target encoding based on the mean of the target variable per category.
Why this is correct
Target encoding reduces cardinality and captures target relationship.
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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