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Self-attention mechanism
Self-attention computes relevance scores between all pairs of positions.
Question 390 of 1,000
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. 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 words in a sentence when generating 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 in the Transformer architecture computes attention scores between every pair of words in the input sequence, allowing the model to dynamically weigh the importance of each word when generating output. This is achieved through scaled dot-product attention, where query, key, and value matrices are used to produce context-aware representations.
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.
- ✓
Self-attention mechanism
Why this is correct
Self-attention computes relevance scores between all pairs of positions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse positional encoding (which provides order information) with self-attention (which provides contextual weighting), leading them to select the wrong component.
Detailed technical explanation
How to think about this question
Under the hood, self-attention computes attention scores as softmax(QK^T / sqrt(d_k))V, where the scaling factor sqrt(d_k) prevents vanishing gradients in the softmax. In practice, multi-head attention runs multiple attention mechanisms in parallel, allowing the model to focus on different subspaces (e.g., syntactic vs. semantic relationships) simultaneously.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — 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 in the Transformer architecture computes attention scores between every pair of words in the input sequence, allowing the model to dynamically weigh the importance of each word when generating output. This is achieved through scaled dot-product attention, where query, key, and value matrices are used to produce context-aware representations.
What should I do if I get this AIF-C01 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.
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Last reviewed: Jul 4, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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