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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
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
  • 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 5
  • 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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Amazon AWS Certified AI Practitioner Sample Questions (Q183-Q188):

NEW QUESTION # 183
A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?

Answer: C

Explanation:
* Fine-tuning requires paired input-output examples to teach the model how to respond to inputs with desired styled outputs.
* Single inputs (A) or outputs (B) are insufficient.
* Separate, unpaired samples (D) don't establish the input-output mapping.
# Reference:
AWS Documentation - Preparing data for fine-tuning FMs


NEW QUESTION # 184
Which prompting attack directly exposes the configured behavior of a large language model (LLM)?

Answer: C

Explanation:
Comprehensive and Detailed
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


NEW QUESTION # 185
Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?

Answer: A


NEW QUESTION # 186
Which option is a characteristic of AI governance frameworks for building trust and deploying human- centered AI technologies?

Answer: C

Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Explanation:
* Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
* Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
* Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
* Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
References:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)


NEW QUESTION # 187
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

Answer: B

Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)


NEW QUESTION # 188
......

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