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

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Executor's log file
B) Ganglia
C) Driver's log file
D) Workspace audit logs
E) Cluster Event Log

2. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
B) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
C) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
D) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.

3. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Increase spark.sql.shuffle.partitions from 200 to 10000.
B) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
C) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
D) Force a sort-merge join with /*+ MERGE(r) */.

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

5. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
B) Use the "Validate" option in the notebook to check for syntax errors.
C) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D) Open the web terminal from the notebook and run a shell command to validate the pipeline code.

Solutions: