SAP-C02 第 157 题
题目
A manufacturing company is building an inspection solution for its factory. The company has IP cameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images. The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factory’s internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers. How should the company deploy the ML model to meet these requirements?
中文翻译：
一家制造公司正在为其工厂构建检查解决方案。该公司在每条装配线的末端都配备了 IP 摄像头。该公司已使用 Amazon SageMaker 训练机器学习 (ML) 模型，以识别静态图像中的常见缺陷。该公司希望在检测到缺陷时向工厂工人提供本地反馈。即使工厂的互联网连接中断，公司也必须能够提供此反馈。该公司有一个本地 Linux 服务器，托管一个 API，为员工提供本地反馈。公司应该如何部署ML模型来满足这些需求？
选项
A. Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.
中文翻译：
设置从每个 IP 摄像机到 AWS 的 Amazon Kinesis 视频流。使用 Amazon EC2 实例拍摄流的静态图像。将图像上传到 Amazon S3 存储桶。使用 ML 模型部署 SageMaker 端点。上传新图像时调用 AWS Lambda 函数来调用推理终端节点。配置 Lambda 函数以在检测到缺陷时调用本地 API。
B. Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.
中文翻译：
在本地服务器上部署 AWS IoT Greengrass。将 ML 模型部署到 Greengrass 服务器。创建一个 Greengrass 组件以从相机获取静态图像并运行推理。配置组件以在检测到缺陷时调用本地 API。
C. Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.
中文翻译：
订购 AWS Snowball 设备。在 Snowball 设备上部署 SageMaker 终端节点、ML 模型和 Amazon EC2 实例。从相机中拍摄静态图像。从 EC2 实例运行推理。配置实例以在检测到缺陷时调用本地 API。
D. Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.
中文翻译：
在每个 IP 摄像机上部署 Amazon Monitron 设备。在本地部署 Amazon Monitron 网关。将 ML 模型部署到 Amazon Monitron 设备。当检测到缺陷时，使用 Amazon Monitron 运行状况警报从 AWS Lambda 函数调用本地 API。
答案
B
解析
正确答案：B 解析： 本题应选择 B。 正确选项： B. 在本地服务器上部署 AWS IoT Greengrass。将 ML 模型部署到 Greengrass 服务器。创建一个 Greengrass 组件以从相机获取静态图像并运行推理。配置组件以在检测到缺陷时调用本地 API。 选择理由： 该选项最直接地满足题干中的关键约束。做 SAP-C02 题目时，需要同时对照题干里的限定词，例如最高性能、最低运维开销、成本效益、可靠性、可扩展性、...