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

Section	Weight	Objectives
Data Processing	28%	- Data Transformation - ETL Pipelines - Structured Streaming - Spark SQL
Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Databricks Lakehouse Platform	24%	- Data Management - Delta Lake - Lakehouse Architecture - Unity Catalog
Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The event_date column is outside the table's partitioning and Z-ordering scheme.
B. The Delta table lacks optimization that enables dynamic file pruning.
C. The filter condition involves a data type excluded from data skipping support.
D. The filter is executed only after the full data scan, preventing data skipping.

Answer: A

Explanation:
Delta Lake's data skipping relies on partitioning and clustering (such as Z-ordering) on the filtered columns. If event_date is neither a partition column nor included in the table's clustering strategy, Spark must scan most files because file-level statistics cannot be effectively used to prune irrelevant data.

NEW QUESTION # 12
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; the change data feed only tracks inserts and updates not deleted records.
B. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
C. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
E. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Answer: D

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 # 13
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Deep clone the table for the analytics team.
B. Create a new table for the analytics team using a CTAS statement.
C. Give the analytics team direct access to the production table.
D. Shallow clone the table for the analytics team.

Answer: D

Explanation:
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION # 14
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 maximum temperature recording for at least one sensor 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 total average temperature across all sensors 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 average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: E

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.

NEW QUESTION # 15
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.autoBroadcastJoinThreshold
B. spark.sql.adaptive.coalescePartitions.minPartitionNum
C. spark.sql.files.maxPartitionBytes
D. spark.sql.files.openCostInBytes
E. spark.sql.adaptive.advisoryPartitionSizeInBytes

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

NEW QUESTION # 16
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