Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

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.

%sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

%sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

%sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

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