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Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes the correct use of pyspark.sql.functions.broadcast?

A) It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B) It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
C) It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D) It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
E) It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

2. A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A) Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
B) Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
C) Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
D) The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
E) Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.

3. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
B) Schedule a job to execute the pipeline once hour on a new job cluster.
C) Configure a job that executes every time new data lands in a given directory.
D) Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

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

A) In the Query Detail screen, by interpreting the Physical Plan
B) In the Delta Lake transaction log. by noting the column statistics
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

5. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) slidingWindow("event_time", "10 minutes")
B) withWatermark("event_time", "10 minutes")
C) awaitArrival("event_time", "10 minutes")
D) delayWrite("event_time", "10 minutes")
E) await("event_time + `10 minutes'")

Solutions:

Question # 1 Answer: B

Question # 2 Answer: B

Question # 3 Answer: B

Question # 4 Answer: A

Question # 5 Answer: B