- 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.
Question 995 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 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 A is correct because classifier-free guidance (CFG) scale controls how strongly the model adheres to the prompt, and including a style prompt (e.g., 'in the style of Impressionism') directly conditions the generation to produce images consistent with a specific artistic style. This combination allows the research team to balance prompt fidelity (via CFG scale) and stylistic control (via the style prompt), which is essential for generating realistic, style-consistent images in Amazon Bedrock.
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.
- ✓
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
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
AWS often tests the misconception that temperature and top_p are the primary controls for image style consistency, when in fact CFG scale and style prompts are the direct mechanisms for prompt adherence and artistic style in diffusion models.
Detailed technical explanation
How to think about this question
CFG scale works by amplifying the difference between conditional and unconditional logits during sampling; a scale of 7-14 is typical for high prompt adherence, while values above 20 can cause oversaturation. Style prompts leverage the model's learned embeddings from training data, where phrases like 'oil painting' or 'watercolor' activate specific latent features. In Amazon Bedrock's Stable Diffusion models, the CFG scale and style prompt interact non-linearly, so tuning both is critical to avoid artifacts like over-sharpening or style washout.
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
Got this wrong? Here's your next step.
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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: Use a CFG (classifier-free guidance) scale and include a style prompt — Option A is correct because classifier-free guidance (CFG) scale controls how strongly the model adheres to the prompt, and including a style prompt (e.g., 'in the style of Impressionism') directly conditions the generation to produce images consistent with a specific artistic style. This combination allows the research team to balance prompt fidelity (via CFG scale) and stylistic control (via the style prompt), which is essential for generating realistic, style-consistent images in Amazon Bedrock.
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
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