2026 Itcertkr 최신 AIF-C01 PDF 버전 시험 문제집과 AIF-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1i6SW4ujU5ZtGL8tWuqgb2RMlwBIE7d97
Amazon인증 AIF-C01시험패스는 고객님의 IT업계종사자로서의 전환점이 될수 있습니다.자격증을 취득하여 승진 혹은 연봉협상 방면에서 자신만의 위치를 지키고 더욱 멋진 IT인사로 거듭날수 있도록 고고싱할수 있습니다. Itcertkr의 Amazon인증 AIF-C01덤프는 시장에서 가장 최신버전으로서 시험패스를 보장해드립니다.
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Amazon인증AIF-C01시험을 패스함으로 취업에는 많은 도움이 됩니다. Itcertkr는Amazon인증AIF-C01시험패스로 꿈을 이루어주는 사이트입니다. 우리는Amazon인증AIF-C01시험의 문제와 답은 아주 좋은 학습자료로도 충분한 문제집입니다. 여러분이 안전하게 간단하게Amazon인증AIF-C01시험을 응시할 수 있는 자료입니다.
질문 # 22
Which option is a use case for generative AI models?
정답：B
설명：
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
질문 # 23
A company has developed a generative text summarization application by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.
Which metric should the company use to evaluate the accuracy of the model?
정답：C
설명：
The correct answer is C because BERTScore is a commonly used metric to evaluate the semantic similarity between generated text (like summaries) and reference text. It uses contextual embeddings from BERT to compare generated and reference sentences, making it highly suitable for evaluating generative tasks like summarization.
From AWS documentation:
"Amazon Bedrock supports BERTScore for evaluating generative text tasks, such as summarization or translation, by comparing the semantic similarity between the output and a reference." Explanation of other options:
A). AUC is used for binary classification models, not generative text.
B). F1 score is also used for classification problems (precision/recall balance).
D). Real World Knowledge (RWK) score is not a standard or supported evaluation metric in Amazon Bedrock.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Documentation - Model Evaluation Metrics
* AWS ML Specialty Guide - Evaluating Generative Models
* AWS Generative AI Developer Tools
질문 # 24
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.
정답：B
설명：
Comprehensive and Detailed
Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.
Accuracy is model performance.
Recency bias skews towards recent data.
Reliability measures consistency of results, not representation.
Reference:
AWS Responsible AI Guidelines - Data Diversity
질문 # 25
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 # 26
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?
정답：A
설명：
The temperature parameter in a large language model (LLM) controls the randomness of the model's output.
A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
* Option A (Correct): "Decrease the temperature value": This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
* Option B: "Increase the temperature value" is incorrect because it would make the output more random and less consistent.
* Option C: "Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
* Option D: "Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.
AWS AI Practitioner References:
* Understanding Temperature in Generative AI Models: AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.
질문 # 27
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AIF-C01시험준비자료: https://www.itcertkr.com/AIF-C01_exam.html
2026 Itcertkr 최신 AIF-C01 PDF 버전 시험 문제집과 AIF-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1i6SW4ujU5ZtGL8tWuqgb2RMlwBIE7d97