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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) 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.
C) 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.
D) Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
E) Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.

2. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A) Total VMs: 8
50 GB per Executor
20 Cores / Executor
B) Total VMs: 4
100 GB per Executor
40 Cores/Executor
C) Total VMs: 1
400 GB per Executor
160 Cores / Executor
D) Total VMs: 2
200 GB per Executor
80 Cores / Executor

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

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

5. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
C) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
E) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Solutions: