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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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

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 (Q110-Q115):

NEW QUESTION # 110
A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics.
Which ML strategy should the company use to meet these requirements?

A. Supervised learning
B. Semi-supervised learning
C. Reinforcement learning
D. Unsupervised learning

Answer: D

Explanation:
The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Unsupervised learning is used to identify patterns or groupings in data without labeled outcomes. Common applications include clustering, such as grouping similar customers or products based on their characteristics, using algorithms like K-means or hierarchical clustering." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
* Option A: Unsupervised learningThis is the correct answer. Unsupervised learning, specifically clustering, is designed to group similar entities (e.g., customers or products) based on their characteristics without requiring labeled data.
* Option B: Supervised learningSupervised learning requires labeled data to train a model for prediction or classification, which is not applicable here since the task involves grouping without predefined labels.
* Option C: Reinforcement learningReinforcement learning involves training an agent to make decisions through rewards and penalties, not for grouping data. This option is irrelevant.
* Option D: Semi-supervised learningSemi-supervised learning uses a mix of labeled and unlabeled data, but the task here does not involve any labeled data, making unsupervised learning more appropriate.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Unsupervised Learning Algorithms (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Unsupervised Learning (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 111
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 all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
B. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
C. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
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: C

NEW QUESTION # 112
Which functionality does Amazon SageMaker Clarify provide?

A. Identifies potential bias during data preparation
B. Documents critical details about ML models
C. Monitors the quality of ML models in production
D. Integrates a Retrieval Augmented Generation (RAG) workflow

Answer: A

NEW QUESTION # 113
Which metric measures the runtime efficiency of operating AI models?

A. Average response time
B. Training time for each epoch
C. Number of training instances
D. Customer satisfaction score (CSAT)

Answer: A

NEW QUESTION # 114
A company wants to build and deploy ML models on AWS without writing any code.
Which AWS service or feature meets these requirements?

A. Amazon Comprehend
B. Amazon Rekognition
C. AWS DeepRacer
D. Amazon SageMaker Canvas

Answer: D

Explanation:
Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:
Explanation:
Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:
"SageMaker Canvas provides a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code."

NEW QUESTION # 115
......

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