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主題	簡介
主題 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.
主題 2	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.
主題 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.
主題 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.
主題 5	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.

>> Amazon AIF-C01在線題庫 <<

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最新的 AWS Certified AI AIF-C01 免費考試真題 (Q84-Q89):

問題 #84
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Ensure that the model runs frequently.
B. Detect imbalances or disparities in the data.
C. Ensure that the model's inference time is within the accepted limits.
D. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
E. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

答案：B,D

解題說明：
To build and use an AI model responsibly, especially in sensitive applications like loan approvals, it's crucial to address potential biases and ensure transparency:
* Detect imbalances or disparities in the data (Option A): Analyzing the training data for imbalances or disparities is essential. Imbalanced data can lead to models that are biased towards the majority class, potentially disadvantaging certain groups. By identifying and mitigating these imbalances, the company can reduce the risk of biased predictions.
* Evaluate the model's behavior to provide transparency to stakeholders (Option C): Regularly assessing the model's outputs and decision-making processes allows the company to understand how decisions are made. This evaluation fosters transparency, enabling the company to explain model behavior to stakeholders and ensure that the model operates as intended without unintended biases.
Options B, D, and E, while relevant to model performance and evaluation, do not directly address the responsible use of AI concerning bias and transparency.

問題 #85
A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.
Which technique should the company use to optimize the generated responses?

A. Use Retrieval Augmented Generation (RAG).
B. Set the temperature to 1.
C. Decrease the token size.
D. Use few-shot prompting.

答案：A

解題說明：
The company is building a chatbot using an LLM to answer questions about HR policies, with access to a large digital documentation base. Retrieval Augmented Generation (RAG) optimizes the LLM's responses by retrieving relevant information from the documentation base and using it to generate accurate, contextually grounded answers, reducing hallucinations and improving response quality.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) enhances the performance of large language models by retrieving relevant information from external knowledge bases, such as documentation or databases, and incorporating it into the generation process. This technique ensures responses are accurate and grounded in the provided data, making it ideal for applications like policy chatbots." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
* Option A: Use Retrieval Augmented Generation (RAG).This is the correct answer. RAG leverages the documentation base to provide the LLM with relevant HR policy information, optimizing the chatbot's responses for accuracy and relevance.
* Option B: Use few-shot prompting.Few-shot prompting provides a few examples in the prompt to guide the LLM, but it is less effective than RAG for large documentation bases, as it cannot dynamically retrieve specific policy details.
* Option C: Set the temperature to 1.Setting the temperature to 1 controls the randomness of the LLM' s output but does not optimize responses using external documentation. This option is unrelated to the documentation base.
* Option D: Decrease the token size.Decreasing token size (likely referring to limiting input/output tokens) may reduce response length but does not optimize the quality of responses using the documentation base.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Building Policy Chatbots (https://aws.amazon.com/bedrock/)

問題 #86
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.
Which methodology should the company use to meet these requirements?

A. Supervised learning
B. Reinforcement learning from human feedback (RLHF)
C. Unsupervised learning
D. Reinforcement learning

答案：C

解題說明：
Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.
* Unsupervised Learning:
* This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data.
* In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.
* Why Option B is Correct:
* Handling Unlabeled Data: Unsupervised learning is specifically designed to work with unlabeled data, making it ideal for the company's need to classify customer data.
* Customer Segmentation: Techniques in unsupervised learning can be used to find natural groupings within customer data, such as identifying high-value vs. low-value customers or segmenting based on purchasing behavior.
* Why Other Options are Incorrect:
* A. Supervised learning: Requires labeled data with input-output pairs to train the model, which is not suitable since the company's data is unlabeled.
* C. Reinforcement learning: Focuses on training an agent to make decisions by maximizing some notion of cumulative reward, which does not align with the company's need for customer classification.
* D. Reinforcement learning from human feedback (RLHF): Similar to reinforcement learning but involves human feedback to refine the model's behavior; it is also not appropriate for classifying unlabeled customer data.

問題 #87
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 G series
B. Amazon EC2 C series
C. Amazon EC2 P series
D. Amazon EC2 Trn series

答案：D

解題說明：
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.

問題 #88
A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

A. Transparency.
B. Fairness.
C. Explainability.
D. Privacy and security.

答案：B

解題說明：
Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. References: AWS Responsible AI Framework.

問題 #89
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Amazon AIF-C01在線題庫 &免費下載AIF-C01考題

Amazon AIF-C01 考試大綱：

免費下載AIF-C01考題，AIF-C01參考資料

最新的 AWS Certified AI AIF-C01 免費考試真題 (Q84-Q89):

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