참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 AIF-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1oMFKes8MRsNlR0Fk5Kt8hDx7ECNOS4mE
Amazon인증AIF-C01시험준비를 하고 계시다면Itexamdump에서 출시한Amazon인증AIF-C01덤프를 제일 먼저 추천해드리고 싶습니다. Itexamdump제품은 여러분들이 제일 간편한 방법으로 시험에서 고득점을 받을수 있도록 도와드리는 시험동반자입니다. Amazon인증AIF-C01시험패는Itexamdump제품으로 고고고!
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Amazon인증AIF-C01시험은 현재 치열한 IT경쟁 속에서 열기는 더욱더 뜨겁습니다. 응시자들도 더욱더 많습니다. 하지만 난이도난 전혀 낮아지지 않고 이지도 어려운 시험입니다. 어쨌든 개인적인 지식 장악도 나 정보기술 등을 테스트하는 시험입니다. 보통은Amazon인증AIF-C01시험을 넘기 위해서는 많은 시간과 신경이 필요합니다.
질문 # 152
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
정답：A
설명：
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference":
This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
질문 # 153
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
정답：
설명：
질문 # 154
A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.
Which evaluation metric meets these requirements?
정답：A
설명：
Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.
C is correct:
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide) A (Recall) measures how many actual frauds are caught, but does not minimize false positives.
B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).
질문 # 155
An AI practitioner is building an ML model. The AI practitioner wants to provide model transparency and explainability to stakeholders.
Which solution will meet these requirements?
정답：B
설명：
The verified answer is A. Present the model Shapley values. AWS documentation states that SageMaker Clarify provides feature attributions based on Shapley values. These values can determine the contribution each feature made to model predictions and can be provided for individual predictions or at a global level for the model as a whole. That is exactly what stakeholders need when they ask for transparency and explainability: they need to understand which features influenced the model's decisions and how strongly those features contributed.
AWS also explains that SageMaker Clarify helps explain how ML models make predictions and that transparency is critical for consumers and regulators. Clarify's explainability uses a model-agnostic feature attribution approach and includes a scalable implementation of SHAP. These explanations answer practical stakeholder questions such as why the model made a prediction, how it makes predictions, and which features have the largest influence on model behavior.
Option B is incorrect because accuracy measures predictive correctness, not explainability. A model can be highly accurate while still being opaque. Accuracy alone does not show why a specific prediction was made.
Option C is incorrect because a confusion matrix summarizes classification performance by showing true positives, false positives, true negatives, and false negatives. It is useful for evaluation, but it does not explain individual feature influence or model reasoning.
Option D is incorrect because a secure inference endpoint protects access to the deployed model. Security is important, but it does not provide transparency or explainability.
Therefore, presenting Shapley values is the correct solution because it directly explains feature contributions to model predictions.
질문 # 156
A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.
The company collected a labeled dataset and wants to scale the solution to all product categories.
Which solution meets these requirements?
정답：B
설명：
When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.
* D is correct:
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI)
* A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.
* C (continued pre-training) uses unlabeled data, not labeled.
질문 # 157
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