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Databricks Certified Data Engineer Associate Exam Questions
- Topic 1: Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI/CD workflows.
- Topic 2: ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
- Topic 3: Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.
- Topic 4: Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
- Topic 5: Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
Free Databricks Databricks Certified Data Engineer Associate Exam Actual Questions
Note: Premium Questions for Databricks Certified Data Engineer Associate were last updated On Aug. 16, 2026 (see below)
A data engineering team needs to integrate two data sources into Databricks:
Clickstream events: 5,000 events per second from an Apache Kafka topic
Customer master data: Only changed records every four hours from a Snowflake database
The solution must process clickstream data with latency under 30 seconds and prevent reprocessing customer master data that has not changed.
Which ingestion approach meets these requirements?
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
: The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network.Reference:Databricks architecture overview,Security and Trust Center
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
The data engineering team can use thespark.tablemethod to access the Delta tablesalesin PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing.Thespark.tablemethod works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1.Alternatively, the data engineering team can also use theDeltaTable.forPathmethod to load the Delta table from its path2.Reference:1:SparkSession | PySpark 3.2.0 documentation2:Welcome to Delta Lake's Python documentation page --- delta-spark 2.4.0 documentation
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.
Which table can be created in this scenario?
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