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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	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 5	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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Amazon AWS Certified AI Practitioner Sample Questions (Q118-Q123):

NEW QUESTION # 118
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Adjust the temperature parameter of the model.
B. Modify the training data to mitigate bias.
C. Include fairness metrics for model evaluation.
D. Avoid overfitting on the training data.
E. Apply prompt engineering techniques.

Answer: B,C

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
A . Include Fairness Metrics for Model Evaluation:
Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
C . Modify the Training Data to Mitigate Bias:
Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
Why Other Options are Incorrect:
B . Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
D . Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
E . Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 119
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?

A. Create a recommendation engine.
B. Build an automatic named entity recognition system.
C. Develop a multi-language translation system.
D. Develop a summarization chatbot.

Answer: D

NEW QUESTION # 120
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?

A. Guardrails
B. Knowledge bases
C. Function calling
D. Access controls

Answer: A

Explanation:
Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.
* Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.
* Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.
* Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)

NEW QUESTION # 121
A company wants to enhance response quality for a large language model (LLM) for complex problem- solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.
Which prompt engineering technique meets these requirements?

A. Few-shot prompting
B. Directional stimulus prompting
C. Zero-shot prompting
D. Chain-of-thought prompting

Answer: D

Explanation:
The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.
Option B: Zero-shot promptingZero-shot prompting relies on the LLM's pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.
Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.
Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.
References:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest
/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Prompting
Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/) Below are the corrected and formatted questions based on the provided input, following the specified format.
Each question is aligned with the main topics from the AWS AI Practitioner certification, and answers are provided with comprehensive explanations referencing official AWS documentation or study guides. Since the exact AWS AI Practitioner documents are not publicly available in full, I will rely on authoritative AWS documentation, whitepapers, and blogs available as of May 17, 2025, to ensure accuracy. If specific document excerpts are unavailable, I will use the most relevant AWS resources and clearly note the references.

NEW QUESTION # 122
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?

A. Decrease the epochs.
B. Increase the temperature parameter.
C. Increase the epochs.
D. Decrease the batch size.

Answer: C

NEW QUESTION # 123
......

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New AIF-C01 Dumps Sheet, AIF-C01 Book Free

Amazon AIF-C01 Exam Syllabus Topics:

AWS Certified AI Practitioner free download pdf & AIF-C01 real practice torrent

Amazon AWS Certified AI Practitioner Sample Questions (Q118-Q123):

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