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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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.

Amazon AWS Certified AI Practitioner Sample Questions (Q121-Q126):

NEW QUESTION # 121
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?

A. Amazon Bedrock Agents
B. Amazon Bedrock custom models
C. Amazon Bedrock inference APIs
D. Amazon Bedrock Guardrails

Answer: D

Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
* Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
* Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
* Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine- tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
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: Building Safe AI Applications (https://aws.amazon.com/bedrock/)

NEW QUESTION # 122
A company needs to build its own large language model (LLM) based on only the company's private data.
The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

A. Amazon EC2 C series
B. Amazon EC2 G series
C. Amazon EC2 Trn series
D. Amazon EC2 P series

Answer: C

Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
* Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
* Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
* Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics- intensive applications but does not focus on minimizing environmental impact for training.
* Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner References:
* AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost- effective solution for ML model training.

NEW QUESTION # 123
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?

A. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
C. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
D. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

Answer: D

NEW QUESTION # 124
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?

A. Model size
B. Context window
C. Batch size
D. Temperature

Answer: B

NEW QUESTION # 125
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)

A. Amazon Bedrock
B. Amazon Rekognition
C. Amazon Polly
D. Amazon Comprehend
E. Amazon Lex

Answer: A,D

Explanation:
To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.
* Amazon Bedrock:
* Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.
* The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces like chatbots, not for sentiment analysis.
* C. Amazon Polly: Converts text to speech; it doesn't analyze sentiment.
* E. Amazon Rekognition: Analyzes images and videos, not text.

NEW QUESTION # 126
......

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