Databricks Databricks-Certified-Data-Engineer-Professional Questions & Answers - in .pdf
- Total Q&A: 250
- Update: Jun 02, 2026
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- Vendor: Databricks
- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
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- Total Q&A: 250
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Databricks Certified Data Engineer Professional Sample Questions:
1. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
Which statement describes this implementation?
A) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
C) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
D) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
E) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
2. A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?
A) Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
B) Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
C) Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
D) Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
3. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
A) Spill resulting from attached volume storage being too small.
B) Credential validation errors while pulling data from an external system.
C) Network latency due to some cluster nodes being in different regions from the source data
D) Task queueing resulting from improper thread pool assignment.
E) Skew caused by more data being assigned to a subset of spark-partitions.
4. A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?
A) Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
B) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
C) Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
D) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
5. Which statement regarding spark configuration on the Databricks platform is true?
A) The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
B) Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
C) When the same spar configuration property is set for an interactive to the same interactive cluster.
D) Spark configuration properties can only be set for an interactive cluster by creating a global init script.
E) Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: E | Question # 4 Answer: D | Question # 5 Answer: B |
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