- A
Feed-forward neural network
Why wrong: Feed-forward networks apply non-linear transformations per position but do not perform attention across positions.
- B
Self-attention mechanism
Self-attention computes attention scores between every pair of tokens, enabling the model to focus on relevant parts of the input.
- C
Positional encoding
Why wrong: Positional encoding adds information about token order, but does not weigh importance of words.
- D
Layer normalization
Why wrong: Layer normalization stabilizes training by normalizing activations, not by weighting tokens.
Question 443 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 sequence 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 is the core component of the Transformer architecture that computes attention scores between every pair of positions in the input sequence, allowing the model to dynamically weigh the importance of different words when generating each output token. This enables the model to capture long-range dependencies and contextual relationships without the sequential processing limitations of RNNs.
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.
- ✗
Feed-forward neural network
Why it's wrong here
Feed-forward networks apply non-linear transformations per position but do not perform attention across positions.
- ✓
Self-attention mechanism
Why this is correct
Self-attention computes attention scores between every pair of tokens, enabling the model to focus on relevant parts of the input.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Positional encoding
Why it's wrong here
Positional encoding adds information about token order, but does not weigh importance of words.
- ✗
Layer normalization
Why it's wrong here
Layer normalization stabilizes training by normalizing activations, not by weighting tokens.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that positional encoding is responsible for weighting word importance, when in fact it only provides positional information and does not perform any attention or weighting of token relevance.
Detailed technical explanation
How to think about this question
Under the hood, self-attention computes Query, Key, and Value matrices from the input embeddings, then calculates attention scores via the dot product of Q and K, scaled by the inverse square root of the key dimension, followed by a softmax to produce a probability distribution over all tokens. In real-world scenarios like machine translation, this allows the model to attend to the correct subject in a long sentence even when separated by many words, directly addressing the vanishing gradient problem that plagued earlier architectures.
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.
What to study next
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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 is the core component of the Transformer architecture that computes attention scores between every pair of positions in the input sequence, allowing the model to dynamically weigh the importance of different words when generating each output token. This enables the model to capture long-range dependencies and contextual relationships without the sequential processing limitations of RNNs.
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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