- A
Pre-training from scratch
Why wrong: Pre-training is extremely resource-intensive.
- B
Prompt engineering with few-shot examples
This provides in-context learning with no training cost.
- C
Reinforcement learning from human feedback
Why wrong: RLHF requires significant data and compute.
- D
Full fine-tuning
Why wrong: Full fine-tuning is expensive and requires substantial data.
Question 36 of 1,000
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. 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 wants to adapt a foundation model for a custom domain with very limited labeled data and minimal cost. Which approach is most suitable?
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
✓
Prompt engineering with few-shot examples
Prompt engineering with few-shot examples is the most suitable approach because it allows the company to adapt a foundation model to a custom domain using very limited labeled data and minimal cost. By providing a few input-output examples directly in the prompt, the model can infer the desired task without any weight updates, making it efficient for low-resource scenarios.
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.
- ✗
Pre-training from scratch
Why it's wrong here
Pre-training is extremely resource-intensive.
- ✓
Prompt engineering with few-shot examples
Why this is correct
This provides in-context learning with no training cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reinforcement learning from human feedback
Why it's wrong here
RLHF requires significant data and compute.
- ✗
Full fine-tuning
Why it's wrong here
Full fine-tuning is expensive and requires substantial data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that full fine-tuning is always the best way to adapt a model, but the trap here is that candidates overlook the cost and data requirements, failing to recognize that prompt engineering with few-shot examples is the most efficient when labeled data is scarce and budget is tight.
Detailed technical explanation
How to think about this question
Prompt engineering with few-shot examples works by conditioning the model's autoregressive generation on a context that includes a few task demonstrations, leveraging the model's in-context learning ability. This approach does not modify the model's parameters, so it avoids the need for backpropagation and large labeled datasets, making it ideal for rapid prototyping with minimal cost. In real-world scenarios, this is often used for tasks like text classification or entity extraction where only a handful of labeled examples exist.
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?
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prompt engineering with few-shot examples — Prompt engineering with few-shot examples is the most suitable approach because it allows the company to adapt a foundation model to a custom domain using very limited labeled data and minimal cost. By providing a few input-output examples directly in the prompt, the model can infer the desired task without any weight updates, making it efficient for low-resource scenarios.
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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