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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| Exam Price: | 100 USD |
| Available Languages: | Korean, Japanese, English, Simplified Chinese, Traditional Chinese |
| Passing Score: | 700 (scaled score 100–1000) |
| Exam Duration: | 90 minutes |
| Exam Format: | Ordering, Multiple choice, Multiple response |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
なぜこれほど多くの認定が必要なのですか？認めなければならないことは、あなたが所有する認定資格が増えれば、より良い仕事を獲得し、より多くの給料を得る機会が増えることです。これが、テストAIF-C01認定を取得することの重要性を認識する必要がある理由です。したがって、AIF-C01試験問題の合格率は98％を超えているため、AIF-C01学習ツールは、ユーザーがより速く効率的に参加するために必要な資格試験に合格するのに役立ちます。 AIF-C01実践ガイドを購入するだけで、AIF-C01試験に合格できます。
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質問 # 239
An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.
Which model type meets these requirements?
正解：B
解説：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
A multimodal large language model (LLM) can:
* Accept both text and image inputs
* Understand visual and textual context
* Generate coherent written explanations
AWS generative AI guidance positions multimodal LLMs as the best choice for applications requiring cross- modal understanding and text generation.
Why the other options are incorrect:
* Computer vision (A) does not generate text explanations.
* Diffusion models (C) generate images.
* Text-to-speech (D) converts text to audio.
AWS AI document references:
* Multimodal Foundation Models on AWS
* Building AI Tutors with Generative Models
* Text and Image Understanding with LLMs
質問 # 240
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
正解：A
解説：
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.
質問 # 241
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.
Which solution will meet these requirements with the LEAST development effort?
正解：C
解説：
The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
* Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.
* Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.
* Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.
* Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.
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 and Conversational AI Amazon Bedrock Developer Guide: Building Conversational AI (https://aws.amazon.com/bedrock/)
質問 # 242
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
正解：B
解説：
Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Capable of extracting key phrases, detecting entities, analyzing sentiment, and identifying topics from text data, making it ideal for analyzing customer support interactions.
* Why Option B is Correct:
* Text Analysis Capabilities: Can process large volumes of text to identify common topics, phrases, and sentiment, providing valuable insights.
* Suitable for Customer Support Analysis: Specifically designed to understand the content and meaning of text, which is key for identifying frequently asked questions.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces, not for text analysis.
* C. Amazon Transcribe: Converts speech to text but does not perform text analysis.
* D. Amazon Translate: Used for translating text between languages, not for analyzing content.
質問 # 243
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential dat a. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
正解：D
解説：
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model beingtrained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.
Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner Reference:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.
質問 # 244
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