Cassandra Practice Test 2025 – Comprehensive All-in-One Guide to Exam Success!

Using clustering columns in Cassandra provides a significant benefit by allowing data to be stored in a sorted order within each partition. This organization facilitates efficient read operations, as the data can be retrieved in a sequential manner. When you read sorted data, the process often requires only a single disk seek, significantly enhancing performance. Disk seeks can be a costly operation in terms of time; minimizing them is crucial for optimizing read performance.

While other choices touch on various concepts relevant to database design and storage, they do not highlight this primary feature of clustering columns in Cassandra. For instance, clustering columns do not inherently allow for partition distribution across multiple drives; rather, partitioning is more about how data is distributed across nodes in the cluster. Similarly, changing clustering criteria is not a flexible feature of Cassandra because the data model needs to be defined at table creation. Lastly, while clustering may affect how data is physically stored, it does not optimize write operations through data rearrangement during writes; writes generally append new data to partitions instead of rearranging existing data. Thus, the capability of reading sorted data with minimal disk seeks stands out as a core advantage of using clustering columns.