1Z0-1127-25 LLM Fundamentals Practice Question
Which component of the Transformer architecture allows each token to consider the relevance of every other token in the input sequence?
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
✓
Self-attention
Self-attention computes attention scores between all pairs of tokens, enabling the model to capture dependencies across the entire sequence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Multi-head attention
Why it's wrong here
Multi-head attention runs multiple self-attention operations in parallel, but the core mechanism is still self-attention.
- ✓
Self-attention
Why this is correct
Self-attention directly computes relevance weights between every pair of tokens in the input.
- ✗
Feed-forward network
Why it's wrong here
Feed-forward networks process each token independently and do not model token interactions.
- ✗
Positional encoding
Why it's wrong here
Positional encoding adds information about token order, but does not compute pairwise relevance.
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