A company is building a machine learning pipeline. Data scientists upload raw images to an S3 bucket. An AWS Lambda function is triggered to resize the images. The resized images are then processed by an Amazon SageMaker training job. The pipeline must be fully automated, handle errors gracefully (with retries and exponential backoff), and provide a visual dashboard of the workflow state. Which combination of services and features should be used? (Select TWO)

Use AWS Step Functions to orchestrate the workflow, including the Lambda function and SageMaker job.

Configure the Step Functions state machine with Catch and Retry blocks to handle errors and implement exponential backoff.

Use Amazon SQS to decouple the Lambda function and the SageMaker job.

Use AWS CodePipeline to orchestrate the image processing and model training.

Write a master Lambda function that calls the resizing Lambda and then synchronously calls the SageMaker API.

Use Amazon EventBridge rules to trigger each step sequentially based on S3 event notifications.