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

Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.

Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.

Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the

most recent value for each username.

%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.

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.