1Z0-1127-25 LLM Fundamentals Practice Question
In the self-attention mechanism, what is the role of the 'scaling factor' (division by sqrt(d_k)) in the softmax computation?
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
✓
To prevent the softmax from saturating and producing small gradients
Scaling prevents the dot products from growing too large in magnitude, which would push softmax into regions with extremely small gradients.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To make the attention mechanism translation invariant
Why it's wrong here
Scaling does not provide translation invariance.
- ✓
To prevent the softmax from saturating and producing small gradients
Why this is correct
Scaling avoids large values that cause softmax saturation.
- ✗
To increase the variance of attention scores
Why it's wrong here
Scaling reduces variance to maintain gradient flow.
- ✗
To ensure the sum of attention weights equals 1
Why it's wrong here
Softmax already ensures normalization; scaling does not affect the sum.
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