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100% Pass Databricks - Accurate Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam Online Exam

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Databricks-Certified-Professional-Data-Engineer Study Reference | Latest Databricks-Certified-Professional-Data-Engineer Exam Question

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
B. The source query failed to update properly for three consecutive minutes and then restarted
C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: C

Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified Reference: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.

NEW QUESTION # 26
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

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

A. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B. No: the change data feed only tracks inserts and updates not deleted records.
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Answer: A

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 27
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve.

A. Enable auto-compaction (optimizeWrite and autoCompact).
B. Enable Delta caching.
C. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
D. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next-generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z-ORDER commands) while improving query performance on large tables-particularly for high-ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.

NEW QUESTION # 28
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE DETAIL dev.pii test
B. SHOW TABLES dev
C. SHOW TBLPROPERTIES dev.pii test
D. DESCRIBE EXTENDED dev.pii test
E. DESCRIBE HISTORY dev.pii test

Answer: D

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 29
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. 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.
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. 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.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
E. 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.

Answer: B

Explanation:
The scenario presented involves inconsistent microbatch processing times in a Structured Streaming job during peak hours, with the need to ensure that records are processed within 10 seconds. The trigger once option is the most suitable adjustment to address these challenges:
* Understanding Triggering Options:
* Fixed Interval Triggering (Current Setup):The current trigger interval of 10 seconds may contribute to the inconsistency during peak times as it doesn't adapt based on the processing time of the microbatches. If abatch takes longer to process, subsequent batches will start piling up, exacerbating the delays.
* Trigger Once:This option allows the job to run a single microbatch for processing all available data and then stop. It is useful in scenarios where batch sizes are unpredictable and can vary significantly, which seems to be the case during peak hours in this scenario.
* Implementation of Trigger Once:
* Setup:Instead of continuously running, the job can be scheduled to run every 10 seconds using a Databricks job. This scheduling effectively acts as a custom trigger interval, ensuring that each execution cycle handles all available data up to that point without overlapping or queuing up additional executions.
* Advantages:This approach allows for each batch to complete processing all available data before the next batch starts, ensuring consistency in handling data surges and preventing the system from being overwhelmed.
* Rationale Against Other Options:
* Option A and E (Decrease Interval):Decreasing the trigger interval to 5 seconds might exacerbate the problem by increasing the frequency of batch starts without ensuring the completion of previous batches, potentially leading to higher overhead and less efficient processing.
* Option B (Increase Interval):Increasing the trigger interval to 30 seconds could lead to latency issues, as the data would be processed less frequently, which contradicts the requirement of processing records in less than 10 seconds.
* Option C (Modify Partitions):While increasing parallelism through more shuffle partitions can improve performance, it does not address the fundamental issue of batch scheduling and could still lead to inconsistency during peak loads.
* Conclusion:
* By using the trigger once option and scheduling the job every 10 seconds, you ensure that each microbatch has sufficient time to process all available data thoroughly before the next cycle begins, aligning with the need to handle peak loads more predictably and efficiently.
References
* Structured Streaming Programming Guide - Triggering
* Databricks Jobs Scheduling

NEW QUESTION # 30
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