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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 tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A) The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
B) An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
C) An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
D) No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
E) A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

2. Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A)

B)

C)

D)

3. A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A) They are ignored.
B) They are merged.
C) They are updated.
D) They are deleted.
E) They are inserted.

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) Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
B) 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.
C) Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
D) Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.

5. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

Solutions: