Amazon Web Services SAA-C03 Dumps Questions Answers

To predict the resources needed for manufacturing processes each month using historical values that are currently stored in an Amazon S3 bucket, a solutions architect should use Amazon SageMaker to train a model by using the historical data in the S3 bucket, and deploy an Amazon SageMaker model and create a SageMaker endpoint for inference. Amazon SageMaker is a fully managed service that provides an easy way to build, train, and deploy machine learning (ML) models. The solutions architect can use the built-in algorithms or frameworks provided by SageMaker, or bring their own custom code, to train a model using the historical data in the S3 bucket as input. The trained model can then be deployed to a SageMaker endpoint, which is a scalable and secure web service that can handle requests for predictions from the application. The solutions architect does not need to have any ML experience or manage any infrastructure to use SageMaker.

The solution that meets the requirements with the most operational efficiency is to configure public subnets in the existing VPC and deploy an MSK cluster in the public subnets. This solution allows the data ingestion solution to be publicly available over the internet without creating a new VPC or deploying a load balancer. The solution also ensures that the data in transit is encrypted by enabling mutual TLS authentication, which requires both the client and the server to present certificates for verification. This solution leverages the public access feature of Amazon MSK, which is available for clusters running Apache Kafka 2.6.0 or later versions1.

The other solutions are not as efficient as the first one because they either create unnecessary resources or do not encrypt the data in transit. Creating a new VPC with public subnets would incur additional costs and complexity for managing network resources and routing. Deploying an ALB or an NLB would also add more costs and latency for the data ingestion solution. Moreover, an ALB or an NLB would not encrypt the data in transit by itself, unless they are configured with HTTPS listeners and certificates, which would require additional steps and maintenance. Therefore, these solutions are not optimal for the given requirements.

References:

• Public access - Amazon Managed Streaming for Apache Kafka