Databricks Certified Data Engineer - Professional
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In the context of a Lakehouse architecture, data partitioning plays a crucial role in optimizing query performance and managing large datasets efficiently. Considering a scenario where an organization is dealing with petabytes of sales data spread across multiple regions and years, and they aim to optimize their query performance while minimizing costs. Which of the following best describes the concept of data partitioning and its application in this scenario? Choose the best option.
A
Data partitioning involves dividing a large dataset into smaller, more manageable pieces based on specific criteria such as date or region, enabling queries to scan only relevant partitions, thus improving performance and reducing costs.
B
Data partitioning is the process of compressing data to save storage space, which indirectly improves query performance by reducing the amount of data read from disk.
C
Data partitioning refers to the practice of creating multiple copies of the same data across different storage locations to enhance data availability and fault tolerance, without directly impacting query performance.
D
Data partitioning is the method of encrypting data at rest and in transit to ensure security and privacy, which is unrelated to query performance optimization.