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Databricks Certified Data Engineer Professional Sample Questions:

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

2. Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A) Executor's detail screen and Executor's log files
B) Query's detail screen and Job's detail screen
C) Stage's detail screen and Executor's log files
D) Stage's detail screen and Query's detail screen
E) Driver's and Executor's log files

3. A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A) Rely on Delta Lake schema enforcement to prevent duplicate records.
B) Perform a full outer join on a unique key and overwrite existing data.
C) Set the configuration delta.deduplicate = true.
D) VACUUM the Delta table after each batch completes.
E) Perform an insert-only merge with a matching condition on a unique key.

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

A) In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
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 Query Detail screen, by interpreting the Physical Plan
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) awaitArrival("event_time", "10 minutes")
B) withWatermark("event_time", "10 minutes")
C) await("event_time + `10 minutes'")
D) delayWrite("event_time", "10 minutes")
E) slidingWindow("event_time", "10 minutes")

Solutions: