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Answer: Spark MLlib
The correct choice is Spark MLlib. Here's why: - **Spark SQL** excels in structured data processing with SQL-like queries but isn't tailored for distributed computing or large-scale data processing in machine learning contexts. - **Spark Streaming** is designed for real-time data stream processing, not for batch processing of large datasets across multiple nodes. - **Spark DataFrame** offers a distributed data structure beneficial for structured data manipulation but lacks specialized features for distributed computing or large-scale data processing. - **Spark MLlib** is specifically crafted for machine learning within Spark, featuring algorithms and tools optimized for distributed computing and large-scale datasets, making it the ideal choice for your project's needs.
Author: LeetQuiz Editorial Team
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In a machine learning project that requires processing a vast amount of data spread across multiple nodes, which Spark ML component is optimized for efficient distributed computing and handling of large-scale datasets?
A
Spark SQL
B
Spark Streaming
C
Spark MLlib
D
Spark DataFrame