AIF-C01 Applications of Foundation Models Practice Question
An e-commerce company uses a foundation model to generate personalized email subject lines. The marketing team notices that the subject lines sometimes contain product recommendations that are out of stock. Which action would best reduce the generation of out-of-stock recommendations without retraining the model?
⚠ Common exam trap
AWS often tests the distinction between inference-time interventions (like prompt engineering) and training-time interventions (like fine-tuning), and the trap here is that candidates may confuse RAG (which retrieves external data but does not enforce constraints) with a system prompt that directly instructs the model, leading them to select D instead of C.
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
✓
Add a system prompt that explicitly instructs the model to only recommend products that are in stock.
Adding a system prompt that explicitly instructs the model to only recommend in-stock products directly constrains the model's output at inference time without requiring retraining. This leverages the model's instruction-following capability to filter its generated content based on the provided context, which is a lightweight and immediate solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement a post-processing step to replace out-of-stock recommendations with in-stock alternatives.
Why it's wrong here
Post-processing is reactive and does not prevent the model from generating out-of-stock recommendations in the first place.
- ✗
Fine-tune the model on a dataset of past successful subject lines that only include in-stock products.
Why it's wrong here
Fine-tuning is more expensive and time-consuming than using a system prompt, and the company wants to avoid retraining.
- ✓
Add a system prompt that explicitly instructs the model to only recommend products that are in stock.
Why this is correct
A system prompt can constrain the model's output to follow the instruction, reducing unwanted recommendations.
- ✗
Use a retrieval-augmented generation (RAG) approach to retrieve a list of in-stock products and include it in the prompt.
Why it's wrong here
RAG can help but does not force the model to only use the retrieved list; it may still generate out-of-stock items.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.