2026 DumpTOP 최신 AIF-C01 PDF 버전 시험 문제집과 AIF-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1MDEYWLe_S5AkPJiDeIytUakBqIFA0Hk9
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| Section | Objectives |
|---|---|
| Topic 1: Fundamentals of Generative AI | - Foundation models and prompt engineering basics - AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts |
| Topic 2: Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Core AI and ML concepts - Common ML workflows and lifecycle |
| Topic 3: Applications of Foundation Models | - AI-powered assistants and automation - Use cases for generative AI in business - Content generation and summarization |
| Topic 4: Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
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질문 # 407
A company is building an AI application to automate business processes. The company uses a foundation model (FM) to support the application.
The company needs to select datasets to assess the quality of the AI model's behavior.
Which type of datasets will meet these requirements?
정답：B
설명：
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI and generative AI evaluation guidance emphasizes that assessing the quality and behavior of a foundation model requires representative and diverse evaluation datasets. These datasets should reflect real-world usage patterns, edge cases, and multiple business scenarios to properly evaluate how the model behaves in production.
Using diverse datasets that cover various use cases and usage scenarios allows organizations to:
Evaluate robustness and generalization of the FM
Identify failure modes, bias, and unsafe behavior across different inputs Validate that the model performs consistently across business workflows Why the other options are incorrect:
A may remove meaningful real-world patterns and does not reflect realistic usage.
B risks reinforcing model biases and does not provide independent evaluation.
D does not reflect real or meaningful business scenarios and can distort evaluation results.
AWS AI Study Guide Reference:
AWS Responsible AI evaluation practices
AWS guidance on foundation model testing and validation
질문 # 408
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?
정답：D
설명：
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.
Effective Prompt Engineering:
Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.
By providing explicit instructions in the prompts, the company can guide the AI to generate content that matches the brand's voice and messaging.
Why Option C is Correct:
Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the model's response through the prompt.
Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resource-efficient.
Why Other Options are Incorrect:
A . Optimize the model's architecture and hyperparameters: Improves model performance but does not specifically address alignment with brand voice.
B . Increase model complexity: Adding more layers may not directly help with content alignment.
D . Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content alignment.
질문 # 409
An AI practitioner is developing a recommendation system. The AI practitioner wants to document a business problem, data assumptions, training considerations, and usage risks. The company must follow guidelines for transparency and governance.
Which Amazon SageMaker AI feature will meet these requirements?
정답：D
설명：
Amazon SageMaker Model Cards are specifically designed to support transparency, responsible AI, and governance throughout the machine learning lifecycle. According to AWS AI documentation, Model Cards provide a structured way to document critical information about machine learning models, including intended use cases, business context, training data assumptions, evaluation metrics, ethical considerations, and known limitations or risks. This makes Model Cards the correct feature for the requirements described.
AWS emphasizes that Model Cards help organizations meet internal governance standards and external regulatory or compliance expectations by improving model interpretability and accountability. They allow AI practitioners to clearly explain why a model was built, how it was trained, what data it relies on, and what risks or biases may exist. This aligns directly with the need to document business problems, training considerations, and usage risks.
Other options do not fully address these needs. Amazon SageMaker Model Registry focuses on versioning, approval workflows, and deployment tracking of models, but it does not provide comprehensive documentation for transparency or ethical considerations. Amazon SageMaker Model Monitor is designed to detect data drift, bias drift, and performance degradation in production, not to document assumptions or governance details. Model Dashboard offers a centralized view of deployed models and endpoints but does not provide structured, narrative documentation of model intent or risks.
AWS positions Model Cards as a core component of Responsible AI, enabling organizations to promote fairness, explainability, and trust in machine learning systems. By capturing standardized, auditable documentation, Model Cards ensure that stakeholders-including developers, auditors, and business leaders- can understand how a recommendation system should be used and where caution is required.
질문 # 410
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?
정답：B
설명：
* ISO certifications apply to processes, frameworks, and systems - not individuals or every piece of software.
* When a company is ISO-certified, its development framework and governance processes comply with ISO standards for security, risk, or AI responsibility.
# Reference:
AWS Compliance Programs - ISO
질문 # 411
A company has developed a custom foundation model (FM) and wants to deploy the FM on AWS. The company needs a managed service that provides the ability to load the company ' s proprietary model weights without managing infrastructure.
Which AWS feature or resource will meet these requirements?
정답：A
설명：
Amazon Bedrock Custom Model Import allows organizations to bring supported customized foundation models into Amazon Bedrock for managed inference.
This feature is particularly useful when a company has already trained or customized a foundation model and possesses proprietary model weights.
The company can import a supported model architecture and use Amazon Bedrock inference capabilities without provisioning or maintaining the underlying inference infrastructure.
Custom Model Import supports supported open-source architectures and allows organizations to preserve model customizations developed outside Amazon Bedrock.
Amazon SageMaker JumpStart provides access to pretrained models and deployment workflows but is not the most direct solution for importing proprietary foundation model weights into Bedrock.
Amazon EC2 requires greater infrastructure management, while AWS Lambda is not designed for general- purpose hosting of large foundation models.
Therefore, Amazon Bedrock Custom Model Import satisfies the requirements.
질문 # 412
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