- A
Use a CFG (classifier-free guidance) scale and include a style prompt
CFG scale controls how closely the image follows the prompt; a style prompt (e.g., 'in the style of Monet') ensures artistic consistency.
- B
High temperature, low top_p
Why wrong: These are text generation parameters; image models use different settings.
- C
Low temperature, high top_p
Why wrong: Temperature and top_p are parameters for text generation, not image generation.
- D
Increase the number of steps and reduce the number of samples
Why wrong: More steps improve detail but do not enforce a specific style.
Quick Answer
The correct combination is using a CFG (classifier-free guidance) scale along with a style prompt. The CFG scale directly controls how strictly the generated image adheres to your text description—higher values enforce prompt fidelity, while lower values allow more creative freedom—and adding a style prompt explicitly anchors the output to a specific artistic direction, such as “in the style of Van Gogh” or “watercolor illustration.” On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of image generation parameters versus text generation parameters; a common trap is confusing temperature or top_p (which control randomness in text models) with the CFG scale, or assuming that simply increasing inference steps will guarantee style consistency when it only improves overall image quality. Remember the mnemonic: “CFG for prompt cling, style prompt for the artistic thing.”
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 research team needs to generate high-quality images with Amazon Bedrock that are realistic and consistent with a specific artistic style. Which combination of parameters should they use?
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
Use a CFG (classifier-free guidance) scale and include a style prompt
Option D is correct because for image generation models like Stable Diffusion XL, the CFG (classifier-free guidance) scale controls adherence to the prompt, and a style prompt can enforce artistic consistency. Option A and B (temperature, top_p) are for text models. Option C (increasing steps) improves quality but not style consistency.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a CFG (classifier-free guidance) scale and include a style prompt
Why this is correct
CFG scale controls how closely the image follows the prompt; a style prompt (e.g., 'in the style of Monet') ensures artistic consistency.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
High temperature, low top_p
Why it's wrong here
These are text generation parameters; image models use different settings.
- ✗
Low temperature, high top_p
Why it's wrong here
Temperature and top_p are parameters for text generation, not image generation.
- ✗
Increase the number of steps and reduce the number of samples
Why it's wrong here
More steps improve detail but do not enforce a specific style.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
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Fundamentals of Generative AI — study guide chapter
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Fundamentals of Generative AI practice questions
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use a CFG (classifier-free guidance) scale and include a style prompt — Option D is correct because for image generation models like Stable Diffusion XL, the CFG (classifier-free guidance) scale controls adherence to the prompt, and a style prompt can enforce artistic consistency. Option A and B (temperature, top_p) are for text models. Option C (increasing steps) improves quality but not style consistency.
What should I do if I get this AIF-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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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.
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