Fundamentals of Large Language Models →easyMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

What is the role of the softmax function in the output layer of an LLM?

⚠ Common exam trap

Watch out — candidates often confuse the role of softmax with other transformer components like attention or tokenization, especially since all are critical to LLM operation, but only softmax directly converts logits to probabilities in the output layer.

Answer choices

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Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Convert logits to probabilities

The softmax function in the output layer of an LLM converts the raw, unnormalized scores (logits) produced by the final linear layer into a probability distribution over the vocabulary. This allows the model to output a valid probability for each token, where all probabilities sum to 1, enabling sampling or greedy decoding for next-token prediction.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Apply attention
Why it's wrong here
Attention mechanisms use softmax internally but the output layer's softmax serves a different purpose.
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Tokenize input
Why it's wrong here
Tokenization is separate from the output layer.
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Compute gradients
Why it's wrong here
Gradients are computed by backpropagation, not softmax.
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Convert logits to probabilities
Why this is correct
Softmax normalizes logits into a probability distribution.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

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