- A
Positional encoding
Why wrong: Positional encoding injects information about token order, not attention weights.
- B
Self-attention mechanism
Self-attention computes query-key-value dot products to assign importance weights across tokens.
- C
Layer normalization
Why wrong: Layer normalization stabilizes training but does not weigh token importance.
- D
Feed-forward network
Why wrong: Feed-forward networks process each token independently without cross-token interaction.
Question 825 of 991
Self-Attention Mechanism in Transformers
This 1Z0-1127 practice question tests your understanding of llm fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating an output?
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
The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of tokens in the input sequence. These scores determine how much each token should influence the representation of every other token, allowing the model to dynamically weigh the importance of different tokens when generating an output. This is achieved through scaled dot-product attention, where queries, keys, and values are derived from the input embeddings.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Positional encoding
Why it's wrong here
Positional encoding injects information about token order, not attention weights.
- ✓
Self-attention mechanism
Why this is correct
Self-attention computes query-key-value dot products to assign importance weights across tokens.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Layer normalization
Why it's wrong here
Layer normalization stabilizes training but does not weigh token importance.
- ✗
Feed-forward network
Why it's wrong here
Feed-forward networks process each token independently without cross-token interaction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Oracle OCI Generative AI exam often tests the misconception that positional encoding or layer normalization is responsible for weighting token importance, when in fact only the self-attention mechanism performs this dynamic weighting based on content relationships.
Detailed technical explanation
How to think about this question
In the self-attention mechanism, each token generates a query, key, and value vector. The attention scores are computed as the dot product of the query with all keys, scaled by the inverse square root of the key dimension, then passed through a softmax to produce weights that sum to 1. These weights are used to compute a weighted sum of the value vectors, effectively allowing the model to focus on relevant parts of the input. In multi-head attention, this process is repeated in parallel across multiple heads, each learning different relationship patterns.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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LLM Fundamentals — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Self-attention mechanism — The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of tokens in the input sequence. These scores determine how much each token should influence the representation of every other token, allowing the model to dynamically weigh the importance of different tokens when generating an output. This is achieved through scaled dot-product attention, where queries, keys, and values are derived from the input embeddings.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
5 more ways this is tested on 1Z0-1127
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which component of the Transformer architecture allows each token to consider the relevance of every other token in the input sequence?
easy- A.Multi-head attention
- ✓ B.Self-attention
- C.Feed-forward network
- D.Positional encoding
Why B: Self-attention computes attention scores between all pairs of tokens, enabling the model to capture dependencies across the entire sequence.
Variation 2. Which component of the Transformer architecture allows the model to focus on different parts of the input sequence when generating each output token?
easy- ✓ A.Self-attention mechanism
- B.Positional encoding
- C.Feed-forward network
- D.Layer normalization
Why A: Self-attention computes attention scores between all pairs of positions, enabling the model to weigh the importance of different input tokens.
Variation 3. Which component of the Transformer architecture allows the model to weigh the importance of different words in a sequence when processing a given word?
easy- ✓ A.Self-attention mechanism
- B.Feed-forward neural network
- C.Positional encoding
- D.Layer normalization
Why A: The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of words in the input sequence. These scores determine how much each word should influence the representation of the current word, allowing the model to dynamically weigh the importance of different words regardless of their positional distance. This mechanism is what enables the Transformer to capture long-range dependencies and contextual relationships in parallel.
Variation 4. Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating each output token?
easy- A.Feed-forward neural network
- B.Multi-head attention
- ✓ C.Self-attention mechanism
- D.Positional encoding
Why C: The self-attention mechanism computes attention scores between all pairs of tokens, enabling the model to dynamically focus on relevant parts of the input. Positional encoding adds order information, multi-head attention runs multiple attention heads in parallel, and the feed-forward network processes each position independently.
Variation 5. Which component of the transformer architecture allows the model to weigh the importance of different words in a sentence when processing input?
easy- A.Layer normalization
- B.Positional encoding
- ✓ C.Self-attention mechanism
- D.Feed-forward neural network
Why C: The self-attention mechanism is the core component of the transformer architecture that enables the model to dynamically assign weights to each word in a sentence relative to every other word. This allows the model to capture contextual relationships and dependencies, such as determining which words are most relevant to the current word being processed, regardless of their distance in the sequence.
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Last reviewed: Jul 4, 2026
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