AIF-C01시험합격, AIF-C01완벽한시험공부자료

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Amazon AIF-C01 시험요강:

주제소개
주제 1
  • 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.
주제 2
  • 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.
주제 3
  • 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.
주제 4
  • 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.
주제 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.

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최신 AWS Certified AI AIF-C01 무료샘플문제 (Q278-Q283):

질문 # 278
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model

정답：

설명：

Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.


질문 # 279
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?

정답：B

설명：
Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.
* Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.
* Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.
* Option C: "Residual neural network" is primarily used for improving the performance of deep networks, not for generating synthetic data.
* Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.
AWS AI Practitioner References:
* GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.


질문 # 280
A company is using Amazon Bedrock Agents to build an application to automate business workflows.

정답：D

설명：
The correct answer is D. Amazon Bedrock Agents are used to orchestrate and execute complex workflows by connecting foundation models with APIs, databases, and tools. According to AWS documentation, agents interpret user inputs, plan the necessary steps, call external APIs or systems, and return structured results. This allows the model to go beyond text generation into full automation workflows-such as booking tasks, querying internal systems, or summarizing reports. Option A describes multi-modal models, B refers to prompt tuning, and C misstates control delegation; agents act autonomously based on model reasoning. Thus, Bedrock Agents function as intelligent orchestrators, handling multi-step task execution through integrated tool use.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Agents Overview
AWS Generative AI Best Practices - Workflow Orchestration


질문 # 281
An online learning company with large volumes of education materials wants to use enterprise search.

정답：D


질문 # 282
A company is building a conversational AI assistant by using Amazon Bedrock AgentCore. The assistant must maintain context across multiple user interactions without requiring the company to manage infrastructure.
Which AgentCore feature meets these requirements?

정답：D

설명：
The verified answer is C. Memory . In Amazon Bedrock AgentCore, Memory is the feature designed to preserve conversational context and make an assistant remember relevant information across interactions.
AWS documentation states that AgentCore Memory supports both short-term and long-term memory. Short- term memory stores raw interactions that help the agent maintain context within a single session, while long- term memory automatically extracts and stores key insights from conversations across multiple sessions, including user preferences, important facts, and session summaries. This directly matches the requirement because the assistant must maintain context across multiple user interactions without the company building and operating its own memory infrastructure.
Option A. Gateway is incorrect because AgentCore Gateway is used to connect agents securely to tools, APIs, and enterprise services. It helps expose external capabilities to agents, but it does not provide persistent conversational context. Option B. Browser Tool is incorrect because the Browser Tool enables an agent to interact with websites or web-based workflows. It is useful for browser automation and web interaction, not memory retention. Option D. Code Interpreter is incorrect because Code Interpreter allows the agent to execute code for tasks such as calculations, file analysis, and data manipulation. It does not solve the requirement of remembering prior conversation details.
The key phrase in the question is "maintain context across multiple user interactions." That is exactly the purpose of AgentCore Memory. AWS also explains that long-term memory provides persistent storage for session-specific context, enabling agents to maintain continuity and personalization across interactions.
Therefore, when a conversational assistant needs continuity, personalization, and context retention without custom infrastructure management, the correct AgentCore feature is Memory .


질문 # 283
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