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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited

Question 2

A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
E. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Question 3

A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
D. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Question 4

A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
B. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster
C. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
D. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Question 5

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. 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.
B. 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.
C. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
D. 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.
E. 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.

Solutions: