Which of the following actions can you perform using the built-in debugger in Databricks?

Deploy the code to a production cluster

Set breakpoints to pause code execution at specific lines

Automatically optimize the code for performance

Automatically generate test cases for the code

What is the purpose of the 'Update' action when managing a Lakeflow Spark Declarative Pipeline?

To modify the pipeline's source code

To change the compute resources allocated to the pipeline

To run the pipeline and process new data

To delete the pipeline and all its associated data

What is a key advantage of using Lakeflow Spark Declarative Pipelines over raw Structured Streaming code?

Lakeflow pipelines are faster than Structured Streaming

Structured Streaming cannot handle streaming data

Lakeflow pipelines do not support batch processing

Lakeflow pipelines provide a declarative interface that manages the entire data flow

Which of the following is a valid way to trigger a Lakeflow Spark Declarative Pipeline?

Through the UI, REST API, or CLI

A data engineer is debugging a notebook that is part of a Lakeflow Spark Declarative Pipeline. The notebook is failing at a specific transformation step. The engineer wants to step through the code line by line to inspect the intermediate DataFrames. Which tool should they use?

The pipeline events API to view logs.

The built-in debugger in Databricks notebooks.

A third-party IDE like VS Code with the Databricks extension.

The Spark UI to view the DAG and stage details.

DatabricksCertified Data Engineer Professional

Domain 1Objective 2

Building and Testing an ETL Pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 1)

Part of the Developing Code for Data Processing using Python and SQL domain, which accounts for 22% of the DATA-ENGINEER-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~11–18 in this domain), expect 6–9 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

11concepts

22%of the exam

Curriculum map

Developing Code for Data Processing using Python and SQL
- Using Python and Tools for development
- Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform
  12 3 4 5 6 ›
Data Ingestion & Acquisition
Data Transformation, Cleansing, and Quality
Data Sharing and Federation
Monitoring and Alerting
5 more domains

11 Concepts in This Objective

Lakeflow Spark Declarative Pipelines fundamentals
Autoloader for incremental data ingestion
Managing pipeline lifecycle
Automating ETL with Jobs
Streaming tables vs materialized views
AUTO CDC APIs for change data capture
Choosing between Structured Streaming and Lakeflow Pipelines
Control flow operators in pipelines
Pipeline configuration for environments and dependencies
Unit and integration testing for pipelines
Using the built-in debugger for pipeline code

Questions 1–5

1foundation · easy
Which of the following actions can you perform using the built-in debugger in Databricks?
Select an answer first
2foundation · easy
What is the purpose of the 'Update' action when managing a Lakeflow Spark Declarative Pipeline?
Select an answer first
3foundation · easy
What is a key advantage of using Lakeflow Spark Declarative Pipelines over raw Structured Streaming code?
Select an answer first
4foundation · easy
Which of the following is a valid way to trigger a Lakeflow Spark Declarative Pipeline?
Select an answer first
5application · medium
A data engineer is debugging a notebook that is part of a Lakeflow Spark Declarative Pipeline. The notebook is failing at a specific transformation step. The engineer wants to step through the code line by line to inspect the intermediate DataFrames. Which tool should they use?
Select an answer first

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