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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?

A) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
B) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
C) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
D) Require the analytics team to use a tool that supports Delta table.

2. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A) Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
B) Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
C) The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
D) Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
E) Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

3. A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
B) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
C) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D) Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.

4. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define units test and functions within the same notebook
B) Define and unit test functions using Files in Repos
C) Define and import unit test functions from a separate Databricks notebook
D) Run unit tests against non-production data that closely mirrors production

5. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
B) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
C) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
D) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
E) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

Solutions: