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Amazon AIF-C01 考試大綱：

主題	簡介
主題 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.
主題 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.
主題 3	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.
主題 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.
主題 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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最新的 AWS Certified AI AIF-C01 免費考試真題 (Q239-Q244):

問題 #239
Which AWS feature records details about ML instance data for governance and reporting?

A. Amazon SageMaker Model Monitor
B. Amazon SageMaker Model Cards
C. Amazon SageMaker Debugger
D. Amazon SageMaker JumpStart

答案：B

解題說明：
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and predictionquality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
Reference: Amazon SageMaker Model Cards

問題 #240
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

答案：

解題說明：

Explanation:

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
* Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
* Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
* K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
* Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
* Binary classification: Supervised learning
* Multi-class classification: Supervised learning
* K-means clustering: Unsupervised learning
* Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)

問題 #241
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.
Which technique will improve the model's accuracy?

A. Transfer learning
B. One-shot prompting
C. Retrieval Augmented Generation (RAG)
D. Federated learning

答案：C

解題說明：
A: Transfer learning: This involves pre-training a model on a large dataset and fine-tuning it for a specific task. While effective for general model improvement, it does not specifically address the challenge of incorporating rapidly changing inventory data into the model's responses.
B: Federated learning: This technique trains models across decentralized devices while keeping data localized, primarily for privacy purposes. It is not designed to handle rapidly changing data or improve NLG model accuracy in this context.
C: Retrieval Augmented Generation (RAG): RAG combines a language model with a retrieval mechanism that fetches relevant, up-to-date information (e.g., inventory data) from an external source during inference. This is ideal for scenarios with dynamic data, as it ensures the model's responses are grounded in the latest information, improving accuracy.
D: One-shot prompting: This involves providing a single example to guide the model's output. While useful for specific tasks, it does not scale well for rapidly changing data or ensure consistent accuracy with dynamic inventory updates.
Exact Extract Reference: According to AWS documentation on generative AI techniques, "Retrieval Augmented Generation (RAG) enhances large language models by retrieving relevant documents or data at inference time, enabling the model to generate accurate and contextually relevant responses, especially for dynamic or frequently updated datasets." (Source: AWS Generative AI Glossary, https://aws.amazon.com/what-is/retrieval-augmented-generation/). This directly addresses the need for accuracy with rapidly changing inventory data.
RAG is the most suitable technique for this scenario, as it allows the model to access and incorporate the latest inventory data, making C the correct answer.
Explanation:
The requirement is to improve the accuracy of a natural language generation (NLG) model that relies on rapidly changing inventory data. Let's evaluate the options:
Reference:
AWS Generative AI Glossary: Retrieval Augmented Generation (https://aws.amazon.com/what-is/retrieval-augmented-generation/) AWS Bedrock Documentation (contextual use of RAG in LLMs) AWS AI Practitioner Study Guide (focus on generative AI techniques for dynamic data)

問題 #242
A company has a generative AI application that uses a pre-trained foundation model (FM) on Amazon Bedrock. The company wants the FM to include more context by using company information.
Which solution meets these requirements MOST cost-effectively?

A. Use Amazon Bedrock Agents.
B. Use Amazon Bedrock Knowledge Bases.
C. Choose a different FM on Amazon Bedrock.
D. Deploy a custom model on Amazon Bedrock.

答案：B

解題說明：
Amazon Bedrock Knowledge Bases enable Retrieval Augmented Generation (RAG) by letting you connect external company data sources to your foundation model in a serverless, cost-effective manner-without retraining or fine-tuning. This allows the model to answer questions and generate content grounded in your own documents or data.
* A is correct:
"Knowledge Bases for Amazon Bedrock enable generative AI applications to retrieve and include your company's information for more contextual responses, without the need for expensive retraining or custom models." (Reference: Amazon Bedrock Knowledge Bases)
* B is not cost-effective or guaranteed to add your company's context.
* C (Agents) are for orchestrating workflows, not specifically RAG/context.
* D (Custom model deployment) is costly and unnecessary for just adding context.

問題 #243
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service meets this requirement?

A. AWS Artifact
B. AWS Data Exchange
C. AWS Audit Manager
D. AWS Trusted Advisor

答案：A

解題說明：
The correct answer is AWS Artifact, which is a central resource for accessing AWS compliance documentation, audit reports, and certifications from both AWS and third-party providers, including ISVs.
According to AWS documentation, Artifact supports notifications for newly available reports. Customers can subscribe to notification emails when specific compliance documents or updates are published. This feature is ideal for regulated industries or companies needing third-party verification of compliance and security postures. Artifact provides access to reports such as SOC 2, ISO, PCI, and more, and is commonly used for audit preparation and vendor risk management. In contrast, AWS Audit Manager helps create internal audit frameworks but is not focused on third-party report distribution. AWS Data Exchange is for marketplace data sharing, and Trusted Advisor provides performance and cost optimization recommendations, not compliance document alerts. Thus, AWS Artifact is the purpose-built service that meets this requirement.
Referenced AWS AI/ML Documents and Study Guides:
AWS Artifact Documentation - Compliance Reports and Subscriptions
AWS Security and Compliance Whitepaper - Using Artifact for Third-Party Assurance

問題 #244
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