A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).
An attack that reveals or leaks this prompt template is known as a prompt extraction attack.
The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.
# Reference:
AWS Responsible AI - Prompt Injection & Extraction Attacks

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
* Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
* Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
* Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
* Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner References:
* Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

The correct answer is C - A CSV file that consists of measurement data, which is a classic example of structured data. According to AWS documentation, structured data is data that is organized in a predefined schema, typically stored in tabular formats with fixed rows, columns, and data types. CSV files, relational databases, and spreadsheets fall into this category because their structure allows deterministic querying and processing. Measurement data contained in a CSV file is easy to analyze with SQL, Amazon Athena, Amazon Redshift, or SageMaker Data Wrangler. In contrast, options A and D (text comments and transcriptions) are examples of unstructured data, requiring NLP techniques. Option B (video files) represents unstructured multimedia data, requiring computer vision. Structured data is essential for supervised ML models, business analytics, and statistical modeling. AWS emphasizes that structured data provides the simplest path to feature engineering and model training due to its consistent format.
Referenced AWS Documentation:
* AWS Data Analytics Whitepaper - Structured vs. Unstructured Data
* Amazon SageMaker Data Wrangler Documentation

The correct answer is C, as conversion rate (how many users upgrade to premium) and retention rate (how many stay subscribed) are primary indicators of a generative AI system's business impact and ROI.
According to AWS documentation, evaluating AI performance should go beyond technical accuracy to include business metrics that show real-world value. For a premium service, higher conversion implies successful personalization that attracts free users, while strong retention reflects sustained engagement and user satisfaction. Metrics like ARPU or query reduction are secondary indicators. AWS emphasizes outcome- based measurement to ensure AI initiatives contribute measurable ROI tied to customer adoption, loyalty, and profitability. Therefore, tracking conversion and retention gives the clearest long-term measure of financial success from generative AI.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - ML Business Metrics
AWS AI Adoption Framework - Value Realization and ROI Tracking

Explanation:
Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.
(Reference: AWS Certified AI Practitioner Official Study Guide)
Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.
(Reference: AWS Machine Learning Overview)
Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.
(Reference: AWS Deep Learning on AWS)