A company needs to run inference on archived datasets that are multiple gigabytes in size. The results do not need to be available immediately. Which Amazon SageMaker inference option fits this scenario?

Batch Transform is the ideal choice for this scenario because it is designed for processing large datasets (multiple gigabytes) where immediate results are not required. It efficiently processes an entire dataset in a single job, making it cost-effective and suitable for offline analysis of archived data. Real-time inference is incorrect because it's for immediate, low-latency predictions on individual data points, not large archived datasets. Serverless inference is also for real-time, on-demand predictions, scaling automatically but not optimized for large batch processing. Asynchronous inference is for near real-time predictions where latency can be higher than real-time but still aims for faster results than batch transform, and it's typically for individual requests rather than entire multi-gigabyte datasets.