- A
Fine-tune a larger model to improve accuracy and reduce retries.
Why wrong: Fine-tuning is expensive; a smaller model is cheaper.
- B
Increase the temperature parameter to get shorter responses.
Why wrong: Temperature affects randomness, not length or cost directly.
- C
Select a smaller foundation model that still meets accuracy requirements.
Smaller models have lower per-token costs and are faster.
- D
Cache previous responses to reuse for similar prompts.
Why wrong: Caching can reduce repeated calls but is not the most effective for variable prompts.
Question 888 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.
A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?
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
✓
Select a smaller foundation model that still meets accuracy requirements.
Option C is the most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.
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.
- ✗
Fine-tune a larger model to improve accuracy and reduce retries.
Why it's wrong here
Fine-tuning is expensive; a smaller model is cheaper.
- ✗
Increase the temperature parameter to get shorter responses.
Why it's wrong here
Temperature affects randomness, not length or cost directly.
- ✓
Select a smaller foundation model that still meets accuracy requirements.
Why this is correct
Smaller models have lower per-token costs and are faster.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cache previous responses to reuse for similar prompts.
Why it's wrong here
Caching can reduce repeated calls but is not the most effective for variable prompts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse cost-reduction strategies with performance-enhancing strategies, assuming that fine-tuning or caching always saves money, when in fact the most direct lever is selecting the smallest capable model for the job.
Detailed technical explanation
How to think about this question
Under the hood, inference cost scales roughly linearly with the number of model parameters and the length of the generated tokens. For example, a 70B-parameter model requires significantly more GPU memory and compute per token than a 7B-parameter model, even for identical prompts. In Amazon Bedrock, pricing is per-token for both input and output, so selecting a smaller FM like Claude 3 Haiku (vs. Claude 3 Opus) can reduce costs by 5–10x while still handling straightforward marketing generation tasks effectively.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Select a smaller foundation model that still meets accuracy requirements. — Option C is the most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.
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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