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Databricks Certified Data Engineer Professional Exam

Last Update Dec 6, 2023
Total Questions : 82

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Questions 1

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?



The five Minute Load Average remains consistent/flat


Bytes Received never exceeds 80 million bytes per second


Network I/O never spikes


Total Disk Space remains constant


CPU Utilization is around 75%

Questions 2

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.



"Can Manage" privileges on the required cluster


Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster


Cluster creation allowed. "Can Attach To" privileges on the required cluster


"Can Restart" privileges on the required cluster


Cluster creation allowed. "Can Restart" privileges on the required cluster

Questions 3

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.

Which strategy will yield the best performance without shuffling data?



Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.


Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.


Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.


Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.


Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

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