- A
Fine-tuning the model on domain-specific data
Why wrong: Fine-tuning modifies model weights, not prompt engineering.
- B
Few-shot prompting with example inputs and outputs
Few-shot prompting provides examples in the prompt to guide the model.
- C
Adjusting the temperature parameter
Why wrong: Temperature is a sampling parameter, not a prompt engineering technique.
- D
Zero-shot prompting
Zero-shot prompting gives the model a task description without examples.
- E
Implementing a vector database for retrieval
Why wrong: Vector databases support RAG, which is a system architecture, not a prompt technique.
Question 310 of 1,000
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and 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 developer is using prompt engineering techniques to improve the performance of a text generation model on Amazon Bedrock. Which TWO techniques are examples of prompt engineering? (Select TWO.)
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
✓
Few-shot prompting with example inputs and outputs
Option B is correct because few-shot prompting is a core prompt engineering technique where the developer provides a small set of example inputs and desired outputs within the prompt to guide the model's behavior without modifying the model itself. This technique leverages in-context learning, allowing the model to infer patterns from the examples and apply them to new inputs, which is a direct application of prompt engineering on 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.
- ✗
Fine-tuning the model on domain-specific data
Why it's wrong here
Fine-tuning modifies model weights, not prompt engineering.
- ✓
Few-shot prompting with example inputs and outputs
Why this is correct
Few-shot prompting provides examples in the prompt to guide the model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adjusting the temperature parameter
Why it's wrong here
Temperature is a sampling parameter, not a prompt engineering technique.
- ✓
Zero-shot prompting
Why this is correct
Zero-shot prompting gives the model a task description without examples.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Implementing a vector database for retrieval
Why it's wrong here
Vector databases support RAG, which is a system architecture, not a prompt technique.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The AWS AI Practitioner exam often tests the distinction between prompt engineering (modifying the input prompt) and model configuration or augmentation (e.g., temperature, fine-tuning, RAG), so the trap here is that candidates confuse inference parameters or data retrieval methods with prompt engineering techniques, leading them to select options like adjusting temperature or using a vector database.
Detailed technical explanation
How to think about this question
Prompt engineering techniques like zero-shot and few-shot prompting exploit the model's pre-trained ability to generalize from context without weight updates. In Amazon Bedrock, few-shot prompting can be particularly effective for tasks like classification or translation, where providing 2-5 examples in the prompt can significantly improve accuracy, but the number of examples must be balanced against the model's context window limits (e.g., 4,096 tokens for Claude v2). A subtle behavior is that the order and formatting of examples matter—mixing example types or using inconsistent delimiters can degrade performance, as the model is sensitive to pattern consistency.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Few-shot prompting with example inputs and outputs — Option B is correct because few-shot prompting is a core prompt engineering technique where the developer provides a small set of example inputs and desired outputs within the prompt to guide the model's behavior without modifying the model itself. This technique leverages in-context learning, allowing the model to infer patterns from the examples and apply them to new inputs, which is a direct application of prompt engineering on 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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