A company offering AI solutions has built an application that relies on AWS SageMaker and AWS DeepLens which integrate with Amazon Rekognition for advanced image analysis. Amazon SageMaker is training its models. They plan to orchestrate all processes with several, existing AWS Lambda functions, automate the build and deployment of their machine learning models, and respond automatically as new images are added to an Amazon S3 bucket. As a DevOps engineer, you have to design an automated solution compatible with the current architecture to deal with this use case. Which automation option is the most appropriate in this application?

Answer options:

A.Configure a state machine as a target for a CloudWatch Events rule, configure a trail in AWS CloudTrail to receive those events. Add a layer of AWS Data Pipeline that orchestrates all existing AWS Lambda functions along with the serverless function that responds to Amazon S3 bucket events. Automation and deployment of Amazon SageMaker models can be managed by AWS Step Functions.
B.Configure an AWS Lambda function as a target for a CloudWatch Events rules, configure a trail in AWS CloudTrail to receive those events. Add another layer of AWS Lambda functions that orchestrates all existing AWS Lambda functions along with the serverless function that responds to Amazon S3 bucket events. Automation and deployment of Amazon SageMaker models can be managed by AWS Lambda functions.
C.Configure an AWS Lambda function as a target for a CloudWatch Events rules, configure a trail in AWS CloudTrail to receive those events. Add a layer of AWS Data Pipeline that orchestrates all existing AWS Lambda functions along with the serverless function that responds to Amazon S3 bucket events. Automation and deployment of Amazon SageMaker models can be managed by AWS Lambda functions.
D.Configure a state machine as a target for a CloudWatch Events rule, configure a trail in AWS CloudTrail to receive those events. Add another layer of AWS Step Functions that orchestrates all existing AWS Lambda functions along with the serverless function that responds to Amazon S3 bucket events. Automation and deployment of Amazon SageMaker models can be managed by AWS Step Functions.

Correct Answer: D
This is another example of AWS Step Functions acting as an orchestrator of complex workflows. In this case, there are already some AWS Lambda functions in place and machine learning services. AWS Step Functions can automate the build and deployment of these models and also acts after events in an Amazon S3 bucket. This is the most appropriate automation option in this application.
Incorrect Answers:
Options A, C is incorrect because AWS Data Pipeline does not orchestrate workloads in this sort of scenario.
Option B is incorrect because it is confusing AWS Lambda as a tool to orchestrate this complex set of services. The right service to automate a solution compatible with the current architecture is AWS Step Functions.
References:
https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-cloudwatch-events-s3.html
https://aws.amazon.com/blogs/machine-learning/automated-and-continuous-deployment-of-amazon-sagemaker-models-with-aws-step-functions/