1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Which three statements about transformer architecture are correct? (Choose three.)
⚠ Common exam trap
Oracle often tests the distinction between encoder-decoder and decoder-only architectures, trapping candidates who assume all transformer-based models follow the original encoder-decoder design, when in fact GPT and other autoregressive models use only the decoder stack.
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
✓
The softmax function is used in the attention mechanism to normalize attention scores.
The softmax function is applied to the raw attention scores (the dot products between queries and keys) to convert them into a probability distribution that sums to 1. This normalization allows the model to assign a relative weight to each token in the sequence, ensuring that the weighted sum of values is stable and interpretable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The softmax function is used in the attention mechanism to normalize attention scores.
Why this is correct
Softmax converts attention scores into probabilities.
- ✗
The feed-forward network applies a different set of weights for each token position.
Why it's wrong here
The feed-forward network shares weights across all positions.
- ✓
Positional encodings are necessary because the model is not recurrent.
Why this is correct
Without recurrence, positional info must be added via encodings.
- ✓
The self-attention layer allows the model to weigh the importance of different tokens.
Why this is correct
Self-attention computes attention weights that determine token importance.
- ✗
The encoder-decoder structure is used in GPT models.
Why it's wrong here
GPT uses a decoder-only architecture.
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