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| Topic | Details |
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| Topic 1 | - Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
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| Topic 2 | - Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
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| Topic 3 | - Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
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| Topic 4 | - Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
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| Topic 5 | - Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
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Amazon AWS Certified AI Practitioner Sample Questions (Q377-Q382):
NEW QUESTION # 377
A company wants to generate synthetic data responses for multiple prompts from a large volume of dat a. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
- A. Use Amazon Bedrock batch inference. Generate responses asynchronously.
- B. Use AWS Lambda functions to automate the task. Submit one prompt after another and store each response.
- C. Input the prompts into the model. Generate responses by using real-time inference.
- D. Use Amazon Bedrock agents. Build an agent system to process the prompts recursively.
Answer: A
NEW QUESTION # 378
A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.
- A. Amazon Polly
- B. Amazon Rekognition
- C. AWS HealthScribe
- D. Amazon Q Developer
Answer: C
Explanation:
Comprehensive and Detailed
AWS HealthScribe provides speech-to-text and medical documentation generation, specifically designed for healthcare applications.
Amazon Polly is text-to-speech, not speech-to-text.
Amazon Rekognition is computer vision.
Amazon Q Developer is a generative AI assistant for developers, not healthcare.
Reference:
AWS Documentation - AWS HealthScribe
NEW QUESTION # 379
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
- A. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
- B. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
- C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
- D. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
Answer: B
NEW QUESTION # 380
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
- A. Zero-shot prompting
- B. Complexity-based prompting
- C. Few-shot prompting
- D. Directional stimulus prompting
Answer: D
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context.
It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
NEW QUESTION # 381
An online learning company with large volumes of educational materials wants to use enterprise search.
Which AWS service meets these requirements?
- A. Amazon Personalize
- B. Amazon Comprehend
- C. Amazon Textract
- D. Amazon Kendra
Answer: D
Explanation:
Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
Amazon Textract extracts structured text from scanned docs and PDFs.
Amazon Personalize builds recommendation systems, not search engines.
# Reference:
AWS Documentation - Amazon Kendra
NEW QUESTION # 382
......
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