2026 Actual Databricks Databricks-Certified-Professional-Data-Engineer Dumps, Updated Exam Questions for Guaranteed Pass

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Databricks Databricks-Certified-Professional-Data-Engineer practice quiz with correct answers for search indexing.

1. Question 1

   A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.

   Streaming DataFrame df has the following schema:

   "device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

   Code block:

   

   Choose the response that correctly fills in the blank within the code block to complete this task.

   Ato_interval('event_time', '5 minutes').alias('time')
   

   Bwindow('event_time', '5 minutes').alias('time')
   

   C'event_time'
   

   Dwindow('event_time', '10 minutes').alias('time')
   

   Elag('event_time', '10 minutes').alias('time')
   

   Correct answer: B

   This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is ''5 minutes'', which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as ''time''. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Structured Streaming'' section;Databricks Documentation, under ''WINDOW'' section. https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-structured-streaming.html

   
   

2. Question 2

   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?

   AThe total average temperature across all sensors exceeded 120 on three consecutive executions of the query
   

   BThe recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
   

   CThe source query failed to update properly for three consecutive minutes and then restarted
   

   DThe maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
   

   EThe average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
   

   Correct answer: E

   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.

   
   

3. Question 3

   The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

   AThe job_id and number of times the job has been run are concatenated and returned.
   

   BThe globally unique ID of the newly triggered run.
   

   CThe job_id is returned in this field.
   

   DThe number of times the job definition has been run in this workspace.
   

   Correct answer: B

   Exact extract: ''run_id: The canonical identifier of a run.''

   
   

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4. Question 4

   In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.

   A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.

   Why are the cloned tables no longer working?

   AThe data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
   

   BBecause Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
   

   CThe metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
   

   DRunning vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
   

   Correct answer: C

   In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.

   : Databricks documentation on Delta Lake, particularly the sections on cloning tables (shallow and deep cloning) and data retention with the vacuum command (https://docs.databricks.com/delta/index.html).

   
   

5. Question 5

   Which of the following is true of Delta Lake and the Lakehouse?

   ABecause Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
   

   BDelta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
   

   CViews in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
   

   DPrimary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
   

   EZ-order can only be applied to numeric values stored in Delta Lake tables
   

   Correct answer: B

   https://docs.delta.io/2.0.0/table-properties.html

   Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1.Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1.By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.

   The other options are false because:

   Parquet compresses data column by column, not row by row2.This allows for better compression ratios, especially for repeated or similar values within a column2.

   Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3.Views are logical constructs that are defined by a SQL query on one or more base tables3.Views are not materialized by default, which means they do not store any data, but only the query definition3.Therefore, views always reflect the latest state of the source tables when queried3. However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.

   Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.

   Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.