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| Section | Objectives |
|---|---|
| Topic 1: Fundamentals of Generative AI | - Foundation models and prompt engineering basics - AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts |
| Topic 2: Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
| Topic 3: Applications of Foundation Models | - Use cases for generative AI in business - Content generation and summarization - AI-powered assistants and automation |
| Topic 4: Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Common ML workflows and lifecycle - Data fundamentals for AI/ML |
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NEW QUESTION # 410
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?
Answer: D
Explanation:
Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:
Explanation:
Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:
"You can configure Guardrails for Amazon Bedrock to define denied topics, use content filters, and apply sensitive information filters, offering protection against prompt injection attacks with minimal development effort."
NEW QUESTION # 411
A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing.
Which AWS service meets this requirement?
Answer: A
Explanation:
Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.
* Amazon Textract:
* Extracts text, forms, and tables from scanned documents accurately.
* Ideal for automating the process of converting PDF resumes into plain text format.
* Why Option A is Correct:
* Automation of Text Extraction: Textract is designed to handle large volumes of documents and convert them into machine-readable text, perfect for the company's need.
* Scalability and Efficiency: Supports scalability to handle a growing volume of resumes as the company expands.
* Why Other Options are Incorrect:
* B. Amazon Personalize: Used for creating personalized recommendations, not for text extraction.
* C. Amazon Lex: A service for building conversational interfaces, not for processing documents.
* D. Amazon Transcribe: Used for converting speech to text, not for extracting text from documents.
NEW QUESTION # 412
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
NEW QUESTION # 413
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?
Answer: D
Explanation:
Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:
"You can configure Guardrails for Amazon Bedrock to define denied topics, use content filters, and apply sensitive information filters, offering protection against prompt injection attacks with minimal development effort."
NEW QUESTION # 414
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
Answer: B
Explanation:
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
* Prompt Templates for Security:
* A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
* This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
* Why Option A is Correct:
* Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
* Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
* Why Other Options are Incorrect:
* B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
* C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
* D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.
NEW QUESTION # 415
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