Amazon Web Services Updated SAP-C02 Exam Questions and Answers by luella

The company should configure Amazon FSx for Lustre with an import and export policy. The company should link the new file system to an S3 bucket. The company should install the Lustre client and mount the document store to an Amazon EC2 instance by using NFS. This solution will meet the requirements with the least amount of effort because Amazon FSx for Lustre is a fully managed service that provides a high-performance file system optimized for fast processing of workloads such as machine learning, high performance computing, video processing, financial modeling, and electronic design automation1. Amazon FSx for Lustre can be linked to an S3 bucket and can import data from and export data to the bucket2. The import and export policy can be configured to automatically import new or changed objects from S3 and export new or changed files to S33. This will ensure that the files are available to the public for download within 30 minutes. Amazon FSx for Lustre supports NFS version 3.0 protocol for Linux clients.

The other options are not correct because:

• Migrating the application to an AWS Lambda function would require a lot of effort and may not be feasible for the existing server that generates many documents. Lambda functions have limitations on execution time, memory, disk space, and network bandwidth.
• Setting up an Amazon S3 File Gateway would not work because S3 File Gateway does not support write-back caching, which means that files written to the file share are uploaded to S3 immediately and are not available locally until they are downloaded again. This would not provide fast local access to the files that the server generates and modifies.
• Configuring AWS DataSync to connect to an Amazon EC2 instance would not meet the requirement of making the files available to the public for download within 30 minutes. DataSync is a service that transfers data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect. DataSync tasks can be scheduled to run at specific times or intervals, but they are not triggered by file changes.

References:

• https://aws.amazon.com/fsx/lustre/
• https://docs.aws.amazon.com/fsx/latest/LustreGuide/create-fs-linked-data-repo.html
• https://docs.aws.amazon.com/fsx/latest/LustreGuide/import-export-data-repositories.html
• https://docs.aws.amazon.com/fsx/latest/LustreGuide/mounting-on-premises.html
• https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html
• https://docs.aws.amazon.com/storagegateway/latest/userguide/StorageGatewayConcepts.html
• https://docs.aws.amazon.com/datasync/latest/userguide/what-is-datasync.html

The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports ML inference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.

The other options are not correct because:

• Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory’s internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.
• Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.
• Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.

References:

• https://aws.amazon.com/greengrass/
• https://docs.aws.amazon.com/greengrass/v2/developerguide/use-machine-learning-inference.html
• https://aws.amazon.com/snowball/
• https://aws.amazon.com/monitron/