- A
Average response latency per request.
Low latency is critical for real-time summarization.
- B
Model size in billions of parameters.
Why wrong: Model size affects latency but latency itself is the direct metric.
- C
Maximum input token limit.
Why wrong: Important but secondary to latency and quality.
- D
Output quality and token efficiency for summarization tasks.
High-quality, concise summaries are the primary goal.
- E
Availability of fine-tuning capability for domain adaptation.
Why wrong: Fine-tuning not required for this use case and adds latency.
Question 751 of 1,000
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 TWO factors are most important when selecting a foundation model in Amazon Bedrock for a text summarization task with strict latency requirements?
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
✓
Average response latency per request.
Option A is correct because average response latency per request directly measures how quickly the model generates summaries, which is critical for strict latency requirements. Amazon Bedrock provides latency metrics for each foundation model, and selecting a model with lower average latency ensures the summarization task meets performance SLAs.
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.
- ✓
Average response latency per request.
Why this is correct
Low latency is critical for real-time summarization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Model size in billions of parameters.
Why it's wrong here
Model size affects latency but latency itself is the direct metric.
- ✗
Maximum input token limit.
Why it's wrong here
Important but secondary to latency and quality.
- ✓
Output quality and token efficiency for summarization tasks.
Why this is correct
High-quality, concise summaries are the primary goal.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Availability of fine-tuning capability for domain adaptation.
Why it's wrong here
Fine-tuning not required for this use case and adds latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that model size (parameters) is the primary driver of latency, but in practice, latency depends on inference optimization, model quantization, and hardware, not just parameter count.
Detailed technical explanation
How to think about this question
Under the hood, latency in Amazon Bedrock is influenced by model architecture, hardware acceleration (e.g., AWS Inferentia), and request batching. For text summarization, models like Amazon Titan Text Lite or Anthropic Claude Instant are optimized for low latency, while larger models like Claude 3 Opus may have higher latency due to deeper transformer layers. Real-world scenarios, such as real-time customer support summarization, require sub-second response times, making latency the primary selection criterion.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Average response latency per request. — Option A is correct because average response latency per request directly measures how quickly the model generates summaries, which is critical for strict latency requirements. Amazon Bedrock provides latency metrics for each foundation model, and selecting a model with lower average latency ensures the summarization task meets performance SLAs.
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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