1Z0-1127-25 LLM Fundamentals Practice Question
Which of the following best describes the role of the self-attention mechanism in a Transformer model?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that self-attention inherently encodes positional information, when in fact it is permutation-invariant and relies on separate positional encodings to maintain sequence order.
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
✓
It computes a weighted sum of all input token representations, where weights depend on pairwise compatibility between tokens
The self-attention mechanism computes a weighted sum of all input token representations, where the weights are determined by the pairwise compatibility (attention scores) between tokens. This allows each token to dynamically attend to every other token in the sequence, capturing global dependencies without the limitations of fixed local windows or recurrence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It encodes the order of tokens in the sequence
Why it's wrong here
Positional encoding, not self-attention, handles token order.
- ✓
It computes a weighted sum of all input token representations, where weights depend on pairwise compatibility between tokens
Why this is correct
Self-attention calculates attention scores between every pair of tokens and uses them to aggregate information.
- ✗
It applies a convolutional filter over local windows of tokens
Why it's wrong here
Convolution is not part of self-attention; self-attention considers all tokens globally.
- ✗
It replaces the need for positional encoding by using recurrence
Why it's wrong here
Self-attention is position-agnostic; positional encoding is still required. It does not use recurrence.
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