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Answer: To apply updates or changes to Spark configurations or cluster-level libraries.
Restarting a Databricks cluster is essential for applying configuration changes and resolving runtime issues. It ensures that updates to Spark configurations, library installations, or performance optimizations are effectively implemented. Moreover, restarting can help clear temporary glitches or memory leaks, enhancing the cluster's performance and reliability in data processing workflows within Databricks environments.
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What is a key reason to restart a Databricks cluster?
A
To increase the storage capacity of the cluster‘s attached storage.
B
When the cluster‘s Spark version needs to be downgraded for compatibility reasons.
C
To apply updates or changes to Spark configurations or cluster-level libraries.
D
Restarting is required to apply software updates or patches to the cluster.
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