NCA-GENL Core Machine Learning and AI Knowledge Practice Question
What is the primary motivation for using Position Embeddings in a transformer model?
⚠ Common exam trap
Candidates often confuse position embeddings with token embeddings, assuming transformers naturally understand word order without extra mechanisms, or incorrectly believe attention mechanisms track sequence positions inherently.
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
✓
To enable the self-attention mechanism to recognize the order of tokens.
Because the self-attention mechanism is permutation-invariant, it treats every token as if it were independent of its position. Without explicit position information, a transformer would struggle to understand syntax and order-dependent relationships. Adding position embeddings to input tokens injects this necessary structural information, allowing the model to distinguish between 'The dog bit the man' and 'The man bit the dog,' which is vital for language understanding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To reduce the computational burden of attention calculations.
Why it's wrong here
Position embeddings are additive vectors that do not affect the computational complexity of the attention mechanism. They simply augment the input tokens with positional information, ensuring the model can distinguish order. This has no impact on the mathematical overhead of calculating attention scores across the input sequence.
- ✓
To enable the self-attention mechanism to recognize the order of tokens.
Why this is correct
Self-attention is inherently position-agnostic; it processes inputs as a set. By injecting position embeddings, we provide the model with essential structural information about the sequence order. This allows the model to learn and respect the sequential nature of natural language, which is crucial for grammar and meaning.
- ✗
To optimize the model for inference on NVIDIA Jetson devices.
Why it's wrong here
Position embeddings are a core requirement of the transformer architecture for understanding sequential data, regardless of the target hardware. They are not an optimization specifically for edge devices like Jetson, but a fundamental component that allows the model to function correctly on any device.
- ✗
To perform dimensionality reduction on input vocabulary.
Why it's wrong here
Position embeddings do not reduce the dimensionality of the vocabulary. The embedding layer maps tokens to fixed-size vectors, and position embeddings are added to these vectors. The vocabulary size and the embedding dimension remain independent of the positional encoding mechanism used within the model's architecture.
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Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCA-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 NCA-GENL exam.