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AIF-C01入門知識 & AIF-C01模擬解説集

Xhs1991は長い歴史を持っているAmazonのAIF-C01トレーニング資料が提供されるサイトです。IT領域で長い時間に存在していますから、現在のよく知られていて、知名度が高い状況になりました。これは受験生の皆様を助けた結果です。Xhs1991が提供したAmazonのAIF-C01トレーニング資料は問題と解答に含まれていて、IT技術専門家たちによって開発されたものです。AmazonのAIF-C01認定試験を受けたいのなら、Xhs1991を選ぶのは疑いないことです。

一般的には、IT技術会社ではAmazon AIF-C01資格認定を持つ職員の給料は持たない職員の給料に比べ、15％より高いです。これなので、IT技術職員としてのあなたはXhs1991のAmazon AIF-C01問題集デモを参考し、試験の準備に速く行動しましょう。我々社はあなたがAmazon AIF-C01試験に一発的に合格するために、最新版の備考資料を提供します。

>> AIF-C01入門知識 <<

ユニークなAIF-C01入門知識試験-試験の準備方法-権威のあるAIF-C01模擬解説集

Xhs1991のAmazonのAIF-C01の試験問題と解答はあなたが受験する前にすべての必要とした準備資料を提供しています。AmazonのAIF-C01の認証試験について、あなたは異なるサイトや書籍で色々な問題を見つけることができます。しかし、ロジックが接続されているかどうかはキーです。Xhs1991の問題と解答は初めに試験を受けるあなたが気楽に成功することを助けるだけではなく、あなたの貴重な時間を節約することもできます。

Amazon AIF-C01 認定試験の出題範囲：

トピック
出題範囲

トピック 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.

トピック 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.

トピック 3

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.

トピック 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.

トピック 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 認定 AIF-C01 試験問題 (Q42-Q47):

質問 # 42
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. Federated learning
B. Transfer learning
C. One-shot prompting
D. Retrieval Augmented Generation (RAG)

正解：D

解説：
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:
* 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.
:
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)

質問 # 43
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)

A. Cost optimization
B. Auto scaling inference endpoints
C. Loosely coupled microservices
D. Data protection
E. Threat detection

正解：D、E

解説：
To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.
* Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.
* Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.
* Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.
* Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.
* Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.
AWS AI Practitioner References:
* AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.

質問 # 44
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?

A. Reinforcement learning with rewards for positive customer feedback
B. Supervised learning with a manually curated dataset of good responses and bad responses
C. Unsupervised learning to find clusters of similar customer inquiries
D. Supervised learning with a continuously updated FAQ database

正解：A

質問 # 45
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

A. Increase the model training time.
B. Add hyperparameters to the model.
C. Increase the volume of data that is used in training.
D. Reduce the volume of data that is used in training.

正解：C

解説：
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner Reference:
Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.

質問 # 46
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 Lex
B. Amazon Rekognition
C. Amazon Comprehend
D. Amazon Bedrock
E. Amazon Polly

正解：C、D

解説：
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.

質問 # 47
......

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AIF-C01模擬解説集: https://www.xhs1991.com/AIF-C01.html

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