Correct Options: C. To partition the data processing task into smaller, parallelizable sub-tasks that can be distributed across a cluster of machines, and B. To transform the unstructured input data into a structured format automatically.
MapReduce is designed for efficient processing of large datasets by breaking down tasks into smaller chunks that can be processed in parallel, which is crucial for scalability and handling exponential data growth. While transforming unstructured data into a structured format is a beneficial side effect, the primary purpose is parallel processing.

Why other options are not correct:

• A. To decrease the volume of data by eliminating redundant information: MapReduce does not primarily aim to reduce data size but to process large datasets efficiently.
• D. To apply advanced machine learning techniques to prevent overfitting in the predictive models developed from the dataset: Overfitting is addressed through techniques like regularization, not through MapReduce.