P.S.TopexamがGoogle Driveで共有している無料の2026 Amazon AIF-C01ダンプ：https://drive.google.com/open?id=1Km1xZf-pycaoBKx42FJCBjMr276JGfLQ
AIF-C01試験はTopexamの教材を準備し、高品質で合格率が高く、実際の試験を十分に理解しており、AIF-C01学習教材を長年にわたって作成した専門家によって完了します。彼らは、AIF-C01試験の準備をするときに受験者が本当に必要とするものを非常によく知っています。また、実際のAIF-C01試験の状況を非常によく理解しています。実際の試験がどのようなものかをお知らせします。AIF-C01試験問題のソフトバージョンを試すことができます。これにより、実際の試験をシミュレートできます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of AI and ML | 20% | - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Topic 2: Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models |
| Topic 3: Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Compliance requirements and regulations |
| Topic 4: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety |
| Topic 5: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
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質問 # 308
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?
正解：D
解説：
The correct answer is C because Amazon Nova Reel is the AWS foundation model designed for generative video use cases, providing end-to-end video generation using generative AI, which significantly reduces video creation time and eliminates the need for manual assembly.
According to AWS Bedrock documentation:
" Amazon Nova Reel enables users to generate short-form video content directly from prompts, including the ability to define style, motion, scenes, and transitions - streamlining the generative content creation process.
"
This is the most operationally efficient choice as it does not require stitching together images or using external editing tools.
Explanation of other options:
A and B involve generating intermediate images and then manually creating videos using video editing tools
- not operationally efficient.
D). Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Directory - Nova Models Overview
AWS GenAI Foundation Model Comparison Guide
AWS Generative AI for Creators Whitepaper (2024)
質問 # 309
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
正解：C
解説：
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions.
Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like
"black boxes."
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes." (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Explanation:
* Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
* Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
* Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
* Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
References:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com
/sagemaker/latest/dg/clarify-explainability.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)
質問 # 310
A company is comparing two foundation models (FMs) for a customer service AI assistant. The company wants to evaluate the FMs based on helpfulness, correctness, and tone. The company needs an evaluation technique that is automated, repeatable, and does not require human reviewers.
Which evaluation technique will meet these requirements?
正解：D
解説：
AWS documentation describes LLM-as-a-judge as an automated evaluation technique where a large language model is used to assess the outputs of another model based on qualitative criteria such as helpfulness, correctness, tone, and alignment with expectations. This approach enables scalable and repeatable evaluations without requiring human reviewers.
In this scenario, the company needs to compare two foundation models across subjective dimensions that are difficult to measure using traditional metrics. LLM-as-a-judge allows the evaluator model to score or rank responses using predefined evaluation prompts and criteria, ensuring consistent and automated assessment.
The other options do not meet the requirements. String matching and ROUGE focus on lexical similarity and are unsuitable for evaluating tone or helpfulness in customer service interactions. Retrieval Augmented Generation is an architectural pattern, not an evaluation technique.
AWS highlights LLM-as-a-judge as a practical approach for automated qualitative evaluation of generative AI outputs, making it the correct choice.
質問 # 311
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
正解：
解説：
質問 # 312
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?
正解：C
解説：
The ecommerce company's chatbot, powered by AI, automates customer order submissions and is accessible
24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot's behavior, such as bypassing safeguards or accessing unauthorized information.
This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model's behavior, potentially leading to unauthorized actions or harmful outputs.
Implementing guardrails and input validation can mitigate this risk."
(Source: AWS Bedrock User Guide, Security Best Practices)
Detailed Explanation:
Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot's operation.
Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
References:
AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide
/security.html)
AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)
質問 # 313
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