Amazon SageMaker supports what type of algorithms?

Amazon SageMaker supports common machine learning algorithms that are specifically optimized for large datasets. This capability is integral to SageMaker's design, as it provides a broad range of built-in algorithms that can efficiently handle significant amounts of data while leveraging the scalable infrastructure of AWS.

These algorithms are ready for use in various tasks, including classification, regression, clustering, and reinforcement learning. The optimization for large data sets means that they can operate effectively in distributed environments, taking advantage of the AWS cloud infrastructure to increase speed, efficiency, and scalability during training and inference.

SageMaker also allows for custom algorithms, which can be integrated easily, further enhancing its versatility. This flexibility encourages developers and data scientists to utilize SageMaker for various machine learning applications without being constrained to a narrow set of options, particularly for large datasets where performance is critical.

The other choices don't reflect the strengths and capabilities of Amazon SageMaker accurately, focusing instead on limited scope or mischaracterizations that do not align with SageMaker’s functionalities.