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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes Delta Lake Auto Compaction?

A) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
B) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
C) Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
D) Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
E) Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.

2. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Spill resulting from attached volume storage being too small.
B) Credential validation errors while pulling data from an external system.
C) Network latency due to some cluster nodes being in different regions from the source data
D) Task queueing resulting from improper thread pool assignment.
E) Skew caused by more data being assigned to a subset of spark-partitions.

3. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Run unit tests against non-production data that closely mirrors production
B) Define and unit test functions using Files in Repos
C) Define units test and functions within the same notebook
D) Define and import unit test functions from a separate Databricks notebook

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

A) It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
B) It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
C) It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
D) It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
E) It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

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

Solutions: