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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
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.
Topic 5
  • 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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Amazon AWS Certified AI Practitioner Sample Questions (Q158-Q163):

NEW QUESTION # 158
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.

Answer: B

Explanation:
Comprehensive and Detailed
Retraining or pre-training is costly and unnecessary for just using company policies.
Fine-tuning adapts models but is inefficient for frequently changing company documents.
Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
Reference:
AWS Documentation - RAG with Amazon Bedrock


NEW QUESTION # 159
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.
Which solution will meet these requirements?

Answer: B


NEW QUESTION # 160
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 161
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?

Answer: D

Explanation:
Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the goal of self-improvement based on past interactions and new information.
* Option B (Correct): "Reinforcement learning with rewards for positive customer feedback": This is the correct answer as reinforcement learning enables the chatbot to learn from feedback and adapt its behavior accordingly, providing self-improvement capabilities.
* Option A: "Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions.
* Option C: "Unsupervised learning to find clusters of similar customer inquiries" is incorrect because unsupervised learning does not provide a mechanism for improving responses based on feedback.
* Option D: "Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self-improvement from feedback.
AWS AI Practitioner References:
* Reinforcement Learning on AWS: AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.


NEW QUESTION # 162
A company needs to automate recurring compliance assessments for its AI workloads. The assessments must include documented evidence mapped to regulatory frameworks.
Which AWS service meets these requirements?

Answer: A

Explanation:
The verified answer is A. AWS Audit Manager. The requirement is recurring compliance assessments with documented evidence mapped to regulatory frameworks. AWS documentation states that AWS Audit Manager helps customers continually audit AWS usage to simplify risk and compliance management. It automates evidence collection so customers can assess whether policies, procedures, and controls are operating effectively. AWS also states that Audit Manager provides prebuilt frameworks that structure and automate assessments for compliance standards and regulations.
The key phrase is "documented evidence mapped to regulatory frameworks." AWS Audit Manager creates assessments from frameworks, automatically runs resource assessments, collects data from in-scope AWS accounts, transforms that data into audit-friendly evidence, and attaches it to relevant controls. That is the exact service function being tested. AWS also explains that Audit Manager can generate reports for auditors and supports prebuilt frameworks for standards such as PCI DSS, GDPR, HIPAA, SOC 2, and AWS operational best practices.
AWS Trusted Advisor is incorrect because Trusted Advisor provides recommendations for cost optimization, performance, security, fault tolerance, service quotas, and operational excellence. It is not a compliance- evidence automation service.
AWS Secrets Manager is incorrect because it stores, rotates, and manages secrets such as database credentials and API keys. It does not automate compliance assessments.
Amazon Inspector is incorrect because it scans workloads for software vulnerabilities and unintended network exposure. It is security-focused, but it does not create recurring evidence-mapped compliance assessments.
Therefore, the correct service is AWS Audit Manager.


NEW QUESTION # 163
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