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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

A)

B)

C)

D)

2. An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A) Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.
B) Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.
C) Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
D) Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.

3. A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A) Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
B) Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C) Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D) Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E) Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

4. In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

A)

B)

C)

D)

5. 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) 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.
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) 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.
E) 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.

Solutions: