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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

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 a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 1
400 GB per Executor
160 Cores / Executor
B. Total VMs: 8
50 GB per Executor
20 Cores / Executor
C. Total VMs: 2
200 GB per Executor
80 Cores / Executor
D. Total VMs: 4
100 GB per Executor
40 Cores/Executor

Question 2

A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A. The table will be removed from the catalog and the data will be deleted.
B. An error will occur because Delta Lake prevents the deletion of production data.
C. Data will be marked as deleted but still recoverable with Time Travel.
D. The table will be removed from the catalog but the data will remain in storage.
E. Nothing will occur until a COMMIT command is executed.

Question 3

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

Question 4

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
B. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
C. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
D. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
E. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

Question 5

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
B. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
C. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D. Use the "Validate" option in the notebook to check for syntax errors.

Solutions: