Exactprep AIF-C01 Questions

AWS documentation defines prompt injection as a security and safety risk in which a user crafts input designed to override, manipulate, or bypass system-level instructions, safeguards, or intended model behavior . The example provided is a classic prompt injection attempt, where the user explicitly instructs the AI assistant to ignore prior rules and operate without restrictions.

In this scenario, the attacker is not exploiting training data or causing factual errors, but rather attempting to change the control flow and behavior of the AI system through malicious or manipulative prompts. AWS identifies prompt injection as a critical risk for generative AI systems, especially those exposed to end users through chat interfaces, APIs, or customer-facing applications.

The other options do not apply. Data bias relates to skewed or unfair training data. Hallucination refers to generating incorrect or fabricated information. Data exposure involves leaking sensitive or private data. None of these describe an attempt to override system instructions.

AWS recommends multiple mitigation strategies for prompt injection risks, including instruction hierarchy enforcement, prompt isolation, input validation, output filtering, and grounding responses using techniques such as Retrieval Augmented Generation . AWS also emphasizes the importance of clearly separating system instructions from user inputs to prevent unauthorized behavior changes.

Prompt injection is categorized by AWS as part of Responsible AI and security governance , highlighting the need for robust guardrails when deploying AI assistants in production. Therefore, the correct answer is prompt injection.

The correct answer is C because BERTScore is a commonly used metric to evaluate the semantic similarity between generated text (like summaries) and reference text. It uses contextual embeddings from BERT to compare generated and reference sentences, making it highly suitable for evaluating generative tasks like summarization.

From AWS documentation:

" Amazon Bedrock supports BERTScore for evaluating generative text tasks, such as summarization or translation, by comparing the semantic similarity between the output and a reference. "

Explanation of other options:

A. AUC is used for binary classification models, not generative text.

B. F1 score is also used for classification problems (precision/recall balance).

D. Real World Knowledge (RWK) score is not a standard or supported evaluation metric in Amazon Bedrock.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Documentation – Model Evaluation Metrics

AWS ML Specialty Guide – Evaluating Generative Models

AWS Generative AI Developer Tools

Amazon Comprehend Medical is designed for processing medical records and extracting key clinical entities, useful for summaries. Per the AWS Comprehend Medical documentation:

“Amazon Comprehend Medical enables extraction of relevant medical information from unstructured clinical text such as medications, conditions, and relationships, making it ideal for summarization tasks.”

The correct answer is D because Few-shot prompting involves providing the LLM with a few examples of the expected input-output format. This helps the model learn and mimic the pattern or structure required in the response — such as generating product descriptions that follow a specific template.

From AWS documentation:

" Few-shot prompting helps guide the model to produce structured and domain-specific outputs by supplying a small number of example inputs and corresponding outputs. "

Explanation of other options:

A. Zero-shot prompting provides no examples, which may lead to inconsistent formatting.

B. Chain-of-thought prompting is used to guide reasoning steps, not formatting.

C. One-shot prompting uses a single example, but few-shot typically yields better structure adherence.

Referenced AWS AI/ML Documents and Study Guides:

AWS Prompt Engineering Guide

Amazon Bedrock Developer Documentation – Prompting Techniques

AWS ML Specialty Study Guide – LLM Prompting Patterns