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Databricks Certified Data Engineer Professional Sample Questions:

1. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
B) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
C) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
D) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
E) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

2. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
B) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
C) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
D) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.

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

4. A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

A) Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
B) Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
C) Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
D) Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.

5. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A) Rewrite their code to avoid putting memory pressure on the driver node.
B) Run the notebook on a single node cluster to keep driver from falling.
C) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
D) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.

Solutions: