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

Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.

Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.

Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.

Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.

Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.

Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

It is retained until you export or delete job run logs

It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

t is retained for 60 days, during which you can export notebook run results to HTML

It is retained for 60 days, after which logs are archived

It is retained for 90 days or until the run-id is re-used through custom run configuration