NCP-GENL LLM Architecture Practice Question
Which architectural component is responsible for projecting the model's hidden states back into the vocabulary space to predict the next token?
⚠ Common exam trap
Candidates frequently confuse the LM Head with intermediate Transformer attention blocks or embedding layers, forgetting which component maps hidden states directly to the vocabulary.
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 LM Head linear layer.
The language model head (LM Head) is a final linear projection layer that maps the high-dimensional hidden states from the Transformer blocks into a vector representing the probability distribution over the entire vocabulary. This projection is the final step in the forward pass, converting the learned internal representation into actionable predictions that can be sampled to generate text.
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 Feed-Forward Network.
Why it's wrong here
The FFN is a component within each Transformer layer, not the final projection layer. While it performs feature transformation, it does not map states to the vocabulary size. The FFN maps to the same hidden dimension as the input, whereas the LM head specifically maps to the vocabulary dimension.
- ✗
The Softmax normalization layer.
Why it's wrong here
Softmax is an activation function used to turn raw logits into probability scores. It does not perform the projection from hidden state to vocabulary space. The projection is done by a linear transformation layer, and the softmax is applied to the output of that layer to normalize the distribution.
- ✓
The LM Head linear layer.
Why this is correct
The LM Head is a linear projection layer that maps the final hidden state to the vocabulary size. This is the standard architectural design for generative models, allowing the transformer to map abstract internal features to specific token IDs that correspond to the model's fixed training vocabulary.
- ✗
The positional embedding layer.
Why it's wrong here
The positional embedding layer is used at the beginning of the model to inject information about token order into the hidden states. It is not involved in the final prediction or vocabulary projection. Its role is solely to provide spatial context to the model at the input level.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.