Question 32 of 1,672
MLS-C01 Modeling Practice Question
A company uses SageMaker built-in BlazingText algorithm for text classification. The model performance is poor on the validation set. The data consists of short documents (average 50 words). Which hyperparameter tuning strategy is most likely to improve performance?
⚠ Common exam trap
It's easy for candidates to assume increasing model capacity (e.g., vector dimension) or filtering rare words (minCount) always helps, but for short documents, the hyperparameter controlling context granularity (window size) is the most impactful lever.
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
✓
Decrease window size from 5 to 2
BlazingText's default window size of 5 may be too large for short documents (average 50 words), causing the model to learn overly broad context that dilutes local semantic patterns. Decreasing the window size to 2 forces the model to focus on tighter word co-occurrences, which is more effective for short text classification where local n-gram signals are critical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase bucket size from 0 to 1000000
Why it's wrong here
Bucket size is for subword information; not directly relevant for short text performance.
- ✗
Increase vector dimension from 100 to 300
Why it's wrong here
Increasing dimension may lead to overfitting on short documents without improving performance.
- ✗
Increase minCount from 1 to 5
Why it's wrong here
Increasing minCount filters out rare words, which may be important for short texts.
- ✓
Decrease window size from 5 to 2
Why this is correct
Smaller window size captures local context better for short documents.
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Last reviewed: Jun 24, 2026
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