MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker's built-in BlazingText algorithm for word2vec embeddings. The dataset is a corpus of 10 million documents. After training, the data scientist observes that the learned embeddings do not capture semantic similarity well (e.g., 'king' and 'queen' are not close). Which hyperparameter adjustment is most likely to improve the quality of embeddings?
⚠ Common exam trap
A common mix-up: candidates confuse 'window size' with 'context size' and assume decreasing it helps with similarity, but in reality, a larger window captures broader topical relationships, while a smaller window captures syntactic patterns; for semantic similarity, a moderate to large window is needed.
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
✓
Increase the vector dimensionality
Increasing the vector dimensionality allows the model to capture more nuanced semantic relationships and co-occurrence patterns in the data. With 10 million documents, the default dimensionality (typically 100 or 300) may be insufficient to encode the rich contextual information, so raising it (e.g., to 300 or 500) gives the model more capacity to learn high-quality embeddings where words like 'king' and 'queen' become closer in vector space.
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 the vector dimensionality
Why this is correct
Higher dimensionality allows embeddings to capture more fine-grained semantic relationships.
- ✗
Decrease the window size
Why it's wrong here
Smaller window size focuses on local context, which may not capture broader semantic similarity.
- ✗
Decrease the number of negative samples
Why it's wrong here
Fewer negative samples can reduce the quality of the contrastive learning, leading to poorer embeddings.
- ✗
Increase the learning rate
Why it's wrong here
A high learning rate can cause the model to diverge or not learn stable embeddings.
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