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Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL – Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question

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Showing 25–26 of 26 questions

Question 25 (Topic 1)

A data engineer is optimizing a managed table that suffers from data skew and frequently changing query filter columns. The engineer needs to avoid costly data rewrites when query patterns evolve. The table size is under 1TB. How should data engineer meet this requirement?

Select an option, then click Submit answer.

  • ○ Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
  • ○ Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
  • ○ Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
  • ○ Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing and adapts easily to new filter columns.
Question 26 (Topic 1)

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: 1. Near-real-time monitoring of the latest location, speed, and fuel_level per truck_id for the operations team. 2. 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?

Select an option, then click Submit answer.

  • ○ 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.
  • ○ 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.
  • ○ 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.
  • ○ 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.