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Databricks Certified Data Engineer Professional Sample Questions:

1. 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.

2. A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A) Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
B) Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
C) Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
D) Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
E) Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

3. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
B) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
C) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
D) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

4. Which distribution does Databricks support for installing custom Python code packages?

A) CRAM
B) sbt
C) jars
D) Wheels
E) CRAN
F) nom

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

Solutions: