Get real Certified-Data-Engineer-Professional exam questions for better preparation

Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

• A. Julia
• B. C++
• C. Regex
• D. Scala Datasets
• E. pyspsark.ml.feature

A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

• A. Files are physically rewritten without the deleted rows.
• B. Rows are marked as deleted both in metadata and in files.
• C. Delta automatically removes all cancelled orders permanently.
• D. Rows are marked as deleted in metadata, not in files.

A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

• A. Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
  Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
• B. Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
  Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
• C. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
• D. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.

A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

• A. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
• B. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
• C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
• D. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

• A. Total VMs: 8
  50 GB per Executor
  20 Cores / Executor
• B. Total VMs: 2
  200 GB per Executor
  80 Cores / Executor
• C. Total VMs: 1
  400 GB per Executor
  160 Cores / Executor
• D. Total VMs: 4
  100 GB per Executor
  40 Cores/Executor