1Z0-1127-25 LLM Fundamentals Practice Question
Which component of the Transformer architecture allows the model to focus on different parts of the input sequence when generating each output token?
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
✓
Self-attention mechanism
Self-attention computes attention scores between all pairs of positions, enabling the model to weigh the importance of different input tokens.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Self-attention mechanism
Why this is correct
Self-attention allows each token to attend to all other tokens.
- ✗
Positional encoding
Why it's wrong here
Positional encoding adds information about token order, but does not enable focusing.
- ✗
Feed-forward network
Why it's wrong here
Feed-forward layers process each position independently, without attention.
- ✗
Layer normalization
Why it's wrong here
Layer normalization stabilizes training, not attention.
Go deeper
Related to this question
About these practice questions
One of 768 original 1Z0-1127-25 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 1Z0-1127-25 practice question is part of Courseiva's free Oracle 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 1Z0-1127-25 exam.