Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.

Option A (Correct): "Batch transform": This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.

Option B: "Real-time inference" is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.

Option C: "Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.

Option D: "Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.

AWS AI Practitioner References:

Batch Transform on AWS SageMaker: AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.