Free Databricks Databricks-Certified-Data-Engineer-Professional Exam Dumps Questions & Answers

Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

• A.In the Delta Lake transaction log. by noting the column statistics
• B.In the Query Detail screen, by interpreting the Physical Plan
• C.In the Executor's log file, by gripping for "predicate push-down"
• D.In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
• E.In the Storage Detail screen, by noting which RDDs are not stored on disk

A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

• A.Workspace audit logs
• B.Executor's log file
• C.Ganglia
• D.Cluster Event Log
• E.Driver's log file

A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader.
The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files. Which Auto Loader configuration should the data engineer used to achieve this?

• A.Configure cloudFiles.maxPartitionBytes with 1GB to limit data in each partition.
• B.Configure cloudFiles.maxFilesPerTrigger and estimate the average file size to approximate a size-based throttle of 1 GB.
• C.Configure cloudFiles.maxBytesPerTrigger with 1 GB to place a limit.
• D.Configure cloudFiles.maxSizePerTrigger with 1 GB to place a limit.

The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

• A.
• B.preds.write.mode("append").saveAsTable("churn_preds")
• C.
• D.
• E.preds.write.format("delta").save("/preds/churn_preds")

When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

• A.The group needs to have USE CATALOG and USE SCHEMA on the catalog.
• B.The group should be made OWNER of the catalog.
• C.The group needs to have ALL PRIVILEGES on the catalog.
• D.The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.