1Z0-1127-25 LLM Fundamentals Practice Question
Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating each output token?
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 mechanism
The self-attention mechanism computes attention scores between all pairs of tokens, enabling the model to dynamically focus on relevant parts of the input. Positional encoding adds order information, multi-head attention runs multiple attention heads in parallel, and the feed-forward network processes each position independently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feed-forward neural network
Why it's wrong here
The feed-forward network applies the same transformation to each token independently and does not compute inter-token importance.
- ✗
Multi-head attention
Why it's wrong here
Multi-head attention is an extension that runs multiple self-attention mechanisms in parallel, but the core weighting ability comes from the self-attention mechanism itself.
- ✓
Self-attention mechanism
Why this is correct
The self-attention mechanism computes attention scores between each token and every other token, allowing the model to focus on relevant parts of the input.
- ✗
Positional encoding
Why it's wrong here
Positional encoding injects information about the position of tokens, but does not compute importance weights between tokens.
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