매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
AWS Certified AI Practitioner 온라인 연습
최종 업데이트 시간: 2025년06월06일
당신은 온라인 연습 문제를 통해 Amazon AIF-C01 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 AIF-C01 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 87개의 시험 문제와 답을 포함하십시오.
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Lowering the temperature value in an LLM controls the randomness of the model's output. A lower temperature (close to 0) makes the model's predictions more deterministic and consistent, leading to similar outputs for identical prompts. This is particularly beneficial in tasks like sentiment analysis, where consistency and reliability in responses are crucial.
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AWS Artifact is a service that provides on-demand access to AWS compliance reports, including those from independent software vendors (ISVs). AWS Artifact can notify users when new compliance reports are available, ensuring that the company stays updated and can evaluate its systems and processes accordingly.
D: AWS Data Exchange’s Data Exchange is used for subscribing to and managing third-party data sets. It is not intended for compliance reports or notifications about them.
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Token is the basic unit of generative AI model
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When data in an Amazon S3 bucket is encrypted using SSE-S3 (Server-Side Encryption with Amazon S3 managed keys), the IAM role used by the application (in this case, Amazon Bedrock) must have permissions to access and decrypt the data. Assigning the correct permissions to the role ensures that the Foundation Model (FM) can access the encrypted data.
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The issue described is likely caused by overfitting, where the model performs well on the training dataset but fails to generalize to unseen data. Increasing the volume of training data can help mitigate overfitting by providing the model with more diverse examples, improving its ability to generalize to new data in production.
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Creating a prompt template that teaches the LLM to identify and resist common prompt engineering attacks, such as prompt injection or adversarial queries, helps prevent manipulation. By explicitly guiding the LLM to ignore requests that deviate from its intended purpose (e.g., "You are a helpful assistant. Do not perform any tasks outside your defined scope."), you can mitigate risks like exposing sensitive information or executing undesirable actions.
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An anomaly detection system can analyze patterns and behaviors, such as IP address access patterns, to detect any deviations from the norm, which could indicate suspicious or malicious activity. An anomaly detection model can flag unusual access attempts, such as those from suspicious IP addresses, making it well-suited for threat detection. Fraud forecasting (option D) typically focuses on predicting potential fraud patterns rather than real-time anomaly detecti
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Transfer learning is a machine learning strategy that leverages pre-trained models and adapts them to new but related tasks. This allows the company to avoid building models from scratch, significantly reducing the time and resources required for training. By fine-tuning the pre-trained model on domain-specific data, the company can achieve high performance for the new task without starting from the beginning.
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Inference is the process of using a trained model to make predictions or decisions on new, unseen data. In the case of an object detection model, inference involves feeding a new image into the model, which then analyzes the image and outputs the detected objects and their locations.
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AWS PrivateLink is used to securely access AWS services from a VPC without exposing the traffic to the public internet. This ensures compliance with regulatory standards that prohibit internet access, as all communication happens over the private AWS network.
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The Amazon EC2 Trn series (Trn1 instances) are purpose-built for training machine learning models and are designed to deliver high performance while optimizing energy efficiency. They use AWS Trainium chips, which are specifically engineered for ML training workloads, providing excellent performance per watt and reducing the environmental impact of large-scale training processes.
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Amazon Bedrock provides the ability to log model invocations, including input and output data, for monitoring and troubleshooting purposes. By enabling invocation logging in Amazon Bedrock, the AI practitioner can store logs securely and use them to analyze model behavior and performance.
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