- A
Switch to a larger foundation model that handles details better.
Why wrong: Larger models may produce even more detailed output and increase cost.
- B
Increase the temperature parameter to 0.9 to make the model more deterministic.
Why wrong: Increasing temperature increases randomness, leading to more varied and potentially more verbose output.
- C
Decrease the top_p parameter to 0.1 and keep max tokens low.
Lowering top_p focuses on the most likely tokens, reducing irrelevant details; low max tokens enforces conciseness.
- D
Use a negative prompt specifying 'do not include unnecessary details'.
Why wrong: Negative prompts can be effective, but parameter tuning is a more direct and reliable method.
Question 269 of 1,000
Control Output Verbosity
This AIF-C01 practice question tests your understanding of applications of foundation models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: top_p sampling. 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 automatically generate product descriptions for their e-commerce website. They use a prompt that includes product attributes and a short description as a starting point. Recently, the generated descriptions have become overly verbose, including irrelevant details and sometimes even incorrect product specifications. The team has tried simplifying the prompt and reducing the max tokens, but the issue persists. The descriptions must be concise and accurate. What is the most effective next step to address this problem?
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
✓
Decrease the top_p parameter to 0.1 and keep max tokens low.
Option C is correct because decreasing the top_p parameter to 0.1 forces the model to sample from a smaller, more probable set of tokens, making the output more focused and less likely to include irrelevant details. Keeping max tokens low enforces conciseness. Option A (switching to a larger model) may increase verbosity and cost without guarantee of improvement. Option B (increasing temperature to 0.9) would increase randomness (since higher temperature makes the model less deterministic), potentially worsening the issue. Option D (using a negative prompt) might help but is less reliable than tuning top_p and max tokens.
Key principle: top_p sampling
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a larger foundation model that handles details better.
Why it's wrong here
Larger models may produce even more detailed output and increase cost.
- ✗
Increase the temperature parameter to 0.9 to make the model more deterministic.
Why it's wrong here
Increasing temperature increases randomness, leading to more varied and potentially more verbose output.
- ✓
Decrease the top_p parameter to 0.1 and keep max tokens low.
Why this is correct
Lowering top_p focuses on the most likely tokens, reducing irrelevant details; low max tokens enforces conciseness.
Related concept
top_p sampling
- ✗
Use a negative prompt specifying 'do not include unnecessary details'.
Why it's wrong here
Negative prompts can be effective, but parameter tuning is a more direct and reliable method.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse temperature and top_p: increasing temperature makes output more random (less deterministic), while decreasing top_p makes output more focused by restricting token choices to high-probability tokens.
Trap categories for this question
Command / output trap
Larger models may produce even more detailed output and increase cost.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- top_p sampling
- temperature
- max tokens
- negative prompt
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
top_p sampling
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
Got this wrong? Here's your next step.
Review top_p sampling, then practise related AIF-C01 questions on the same topic to reinforce the concept.
- →
Applications of Foundation Models — study guide chapter
Learn the concepts, then practise the questions
- →
Applications of Foundation Models practice questions
Targeted practice on this topic area only
- →
All AIF-C01 questions
1,000 questions across all exam domains
- →
AWS Certified AI Practitioner AIF-C01 study guide
Full concept coverage aligned to exam objectives
- →
AIF-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AIF-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Applications of Foundation Models practice questions
Practise AIF-C01 questions linked to Applications of Foundation Models.
AI and ML Fundamentals practice questions
Practise AIF-C01 questions linked to AI and ML Fundamentals.
Security, Compliance, and Governance for AI Solutions practice questions
Practise AIF-C01 questions linked to Security, Compliance, and Governance for AI Solutions.
Fundamentals of AI and ML practice questions
Practise AIF-C01 questions linked to Fundamentals of AI and ML.
Fundamentals of Generative AI practice questions
Practise AIF-C01 questions linked to Fundamentals of Generative AI.
Generative AI and Foundation Models practice questions
Practise AIF-C01 questions linked to Generative AI and Foundation Models.
Guidelines for Responsible AI practice questions
Practise AIF-C01 questions linked to Guidelines for Responsible AI.
Security, Compliance and Governance for AI Solutions practice questions
Practise AIF-C01 questions linked to Security, Compliance and Governance for AI Solutions.
AIF-C01 fundamentals practice questions
Practise AIF-C01 questions linked to AIF-C01 fundamentals.
AIF-C01 scenario practice questions
Practise AIF-C01 questions linked to AIF-C01 scenario.
AIF-C01 troubleshooting practice questions
Practise AIF-C01 questions linked to AIF-C01 troubleshooting.
Practice this exam
Start a free AIF-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — top_p sampling.
What is the correct answer to this question?
The correct answer is: Decrease the top_p parameter to 0.1 and keep max tokens low. — Option C is correct because decreasing the top_p parameter to 0.1 forces the model to sample from a smaller, more probable set of tokens, making the output more focused and less likely to include irrelevant details. Keeping max tokens low enforces conciseness. Option A (switching to a larger model) may increase verbosity and cost without guarantee of improvement. Option B (increasing temperature to 0.9) would increase randomness (since higher temperature makes the model less deterministic), potentially worsening the issue. Option D (using a negative prompt) might help but is less reliable than tuning top_p and max tokens.
What should I do if I get this AIF-C01 question wrong?
Review top_p sampling, then practise related AIF-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
top_p sampling
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 →
Keep practising
More AIF-C01 practice questions
- A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practi…
- A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimizati…
- A developer is using the Amazon Bedrock API to generate text. They notice that the model sometimes returns harmful conte…
- Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cau…
- A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?
- A company uses Amazon SageMaker to train a model. The training job fails with 'InsufficientInstanceCapacity' error. What…
Last reviewed: Jun 23, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.