Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the  Table Access Control  feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the  DELETE  command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:

Table Access Control :

DELETE :

The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:

databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl

This will upload the custom Python Wheel to the cluster and make it available for use with a production job. You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.

Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.

Conversely, streaming tables are designed for real-time data ingestion and processing , enabling event-driven analytics and low-latency use cases.

Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C , where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.