SAP-C02 Exam Questions & Answers

A company has implemented an ordering system using an event-dnven architecture. Dunng initial testing, the system stopped processing orders Further tog analysis revealed that one order message in an Amazon Simple Queue Service (Amazon SOS) standard queue was causing an error on the backend and blocking all subsequent order messages The visibility timeout of the queue is set to 30 seconds, and the backend processing timeout is set to 10 seconds. A solutions architect needs to analyze faulty order messages and ensure that the system continues to process subsequent messages

Which step should the solutions architect take to meet these requirements?

A. Increase the backend processing timeout to 30 seconds to match the visibility timeout

B. Reduce the visibility timeout of the queue to automatically remove the faulty message

C. Configure a new SOS FIFO queue as a dead-letter queue to isolate the faulty messages

D. Configure a new SOS standard queue as a dead-letter queue to isolate the faulty messages.

A company is running its solution on AWS in a manually created VPC. The company is using AWS Cloud Formation to provision other parts of the infrastructure. According to a new requirement, the company must manage all infrastructure in an automatic way.

What should the company do to meet this new requirement with the LEAST effort?

A. Create a new AWS Cloud Development Kit (AWS CDK) stack that stnctly provisions the existing VPC resources and configuration. Use AWS CDK to import the VPC into the stack and to manage the VPC.

B. Create a CloudFormation stack set that creates the VPC. Use the stack set to import the VPC into the stack.

C. Create a new CloudFormation template that strictly provisions the existing VPC resources and configuration. From the CloudFormation console, create a new stack by importing the existing resources.

D. Create a new CloudFormation template that creates the VPC. Use the AWS Serverless Application Model {AWS SAM) CLI to import the VPC.

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?

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.

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.

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.

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.