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Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A) An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
B) The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
C) The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
D) A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
E) An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

2. A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A) Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
B) Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
C) Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
D) Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.

3. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A) - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
B) - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
C) - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
D) - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
E) - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

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

5. A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A) Use only folder-level permissions and avoid setting permissions on individual jobs.
B) Grant all users CAN MANAGE permission on all jobs to avoid access issues.
C) Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
D) Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.

Solutions: