Explanation:
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:
Ultimate access to all questions.
No comments yet.
In the context of large-scale data processing, a team is evaluating the use of MapReduce to handle a dataset that is expected to grow exponentially over time. The dataset is unstructured and requires significant processing to extract meaningful insights. The team is also concerned about the cost and scalability of their solution. Given these constraints, which of the following best describes the primary reason for utilizing MapReduce in this scenario? Choose the best option.
A
To decrease the volume of data by eliminating redundant information.
B
To transform the unstructured input data into a structured format automatically.
C
To partition the data processing task into smaller, parallelizable sub-tasks that can be distributed across a cluster of machines.
D
To apply advanced machine learning techniques to prevent overfitting in the predictive models developed from the dataset.
E
Both B and C are correct.