A financial services company is building an automated pipeline to update its fraud detection ML model every week using Amazon SageMaker Pipelines. The pipeline will consist of the following steps: A data preprocessing step to clean and transform transactional data. A model training step to build the fraud detection model. An evaluation step to calculate accuracy and other metrics. A model registration step to store the new model in the SageMaker Model Registry. The preprocessing step involves large-scale data transformations and joins across multiple datasets stored in Amazon S3. The data transformations currently run on a distributed Amazon EMR cluster. The data science team wants to integrate these transformations into the SageMaker Pipelines workflow seamlessly. Which options should be combined for a solution that addresses these requirements? (Select two)