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AIF-C01 Practice Question: A developer is using prompt engineering…
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.)
⚠ Common exam trap
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.
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
Few-shot prompting (B) is a core prompt engineering technique because it places several example input-output pairs directly in the prompt to steer the model's behavior without changing model weights. Zero-shot prompting (D) is likewise prompt engineering, as it crafts the instruction and task description so the model can perform the task with no examples provided. Both operate purely at the prompt/input level, which is the defining characteristic of prompt engineering on Amazon Bedrock. Fine-tuning (A) is excluded because it modifies model parameters through additional training rather than engineering the prompt. Adjusting temperature (C) is an inference parameter (decoding setting), not a prompt construction technique. Implementing a vector database for retrieval (E) is a Retrieval Augmented Generation architecture component, not prompt engineering itself.
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 updates model weights through training on labelled data, altering the model itself rather than the input prompt. It is tempting because it also improves domain performance, but it is a model-customisation technique, not prompt engineering, which only changes the text supplied at inference time.
- ✓
Few-shot prompting with example inputs and outputs
Why this is correct
Few-shot prompting supplies labelled input-output pairs inside the prompt, steering the model toward the desired response format without retraining. This satisfies the stem's requirement for a prompt engineering technique, since it alters only the inference-time prompt on Amazon Bedrock, leaving model weights and parameters unchanged.
- ✗
Adjusting the temperature parameter
Why it's wrong here
Temperature is an inference configuration parameter controlling output randomness, not part of the prompt text. It is tempting because it shapes generation and is often tuned alongside prompts, but prompt engineering modifies the input instructions or examples; temperature is set separately in the model invocation request.
- ✓
Zero-shot prompting
Why this is correct
Zero-shot prompting is a prompt engineering technique: the model receives a task instruction with no worked examples, relying on its pre-trained knowledge to generate the response. It satisfies the stem's requirement for prompt engineering because performance is improved purely by how the input prompt is constructed, not by retraining or fine-tuning the model.
- ✗
Implementing a vector database for retrieval
Why it's wrong here
A vector database supports retrieval-augmented generation by supplying external context; it is an architecture pattern, not a prompt engineering technique. It is correct when the model needs grounding in proprietary or current data beyond its training.
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