HOTSPOT A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket. Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.) • An S3 event notification invokes the pipeline when new data is uploaded. • S3 Lifecycle rule invokes the pipeline when new data is uploaded. • SageMaker retrains the model by using the data in the S3 bucket. • The pipeline deploys the model to a SageMaker endpoint. • The pipeline deploys the model to SageMaker Model Registry. 
1. An S3 event notification invokes the pipeline when new data is uploaded
2. SageMaker retrains the model by using the data in the S3 bucket
3. The pipeline deploys the model to a SageMaker endpoint
Had something like this in a mock. This order makes sense because S3 events are used to trigger automation, then retraining, and finally deploy the fresh model to an endpoint for inference. Pretty sure this is what they want here.
Wow, AWS loves to bury you in their services for these pipelines. The right order is: S3 event notification triggers the pipeline when new data lands, SageMaker does the retraining, then you push the model to a SageMaker endpoint for inference. Pretty standard MLOps pattern here, unless I'm missing something sneaky in their options.
