Databricks · DCDEA
Databricks Certified Data Engineer Associate Practice Tests
Validates the ability to perform data engineering tasks on the Databricks Lakehouse Platform, covering ELT with Spark SQL and PySpark, data pipeline development with Delta Lake and Databricks Workflows, data governance with Unity Catalog, and data quality management.
Exam Details
Practice Questions
628
≈ 13 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Databricks Data Engineer Associate Practice Exam
This Databricks Data Engineer Associate practice exam helps you review lakehouse fundamentals, ELT workflows, Delta Lake behavior, production pipeline concepts, and Databricks SQL. The question bank gives you repeated exposure to the terminology and scenario patterns you are likely to see when preparing for the associate certification.
Focus first on areas where your explanations reveal weak recall: table design, pipeline orchestration, permissions, and performance concepts. As your accuracy improves, use quick quizzes and simulated sessions to confirm that you can answer consistently without relying on long review time.
Exam Domain Breakdown
Databricks Intelligence Platform10%
Development and Ingestion30%
Data Processing and Transformations31%
Productionizing Data Pipelines18%
Data Governance and Quality11%
Exam Overview
The Databricks Certified Data Engineer Associate certification validates a practitioner's ability to use the Databricks Data Intelligence Platform to perform introductory data engineering tasks. The exam covers a broad range of competencies including platform architecture and workspace navigation, data ingestion and ELT development using Apache Spark SQL and PySpark, incremental data processing with Delta Lake and Auto Loader, pipeline orchestration with Databricks Workflows and Lakeflow Declarative Pipelines (formerly Delta Live Tables), and data governance with Unity Catalog.
As of July 25, 2025, the exam was updated to reflect Databricks' evolution toward an AI-driven Data Intelligence Platform. The updated blueprint introduces revised domain terminology and adds newer concepts such as Liquid Clustering, Databricks Asset Bundles (DABs), Delta Sharing, and Lakehouse Federation. Code questions are presented in SQL where possible, with Python (PySpark) used for all other scenarios. The exam costs $200 USD plus applicable local taxes and requires renewal every two years by retaking the current version.
Who Should Take This Exam
This certification is designed for data engineers, analytics engineers, and ETL developers who work with the Databricks platform in a professional capacity. Ideal candidates are those who build, manage, and optimize data pipelines on cloud data platforms and want to validate their foundational Databricks skills.
Databricks recommends at least six months of hands-on experience performing the data engineering tasks outlined in the official exam guide before attempting the exam. Professionals transitioning from traditional data warehouse or ETL backgrounds who are adopting the Lakehouse paradigm will also find this certification a valuable credential to demonstrate their platform proficiency.
Prerequisites
There are no mandatory formal prerequisites to register for the Databricks Certified Data Engineer Associate exam. However, Databricks strongly recommends that candidates have a minimum of six months of hands-on experience performing data engineering tasks on the Databricks platform before sitting for the exam.
Candidates should be comfortable writing queries and transformations in both Spark SQL and PySpark, understand the core concepts of the Databricks Lakehouse architecture, and have practical experience with Delta Lake operations, Auto Loader for incremental ingestion, and Databricks Workflows for job orchestration. Familiarity with Unity Catalog for data governance and access control is also expected under the current July 2025 exam blueprint.
Exam Format
The Databricks Certified Data Engineer Associate exam consists of 45 multiple-choice questions to be completed within 90 minutes, allowing approximately two minutes per question. The exam is delivered online and proctored remotely. The passing score is 70%, meaning candidates must correctly answer at least 32 of the 45 scored questions.
The exam may include a small number of unscored items used to gather statistical data for future exam development; these items are not identified and do not affect the final score, with additional time factored in to account for them. The exam fee is $200 USD plus applicable local taxes. Certification is valid for two years, after which recertification requires retaking the current version of the exam.
Skills Measured
- 1.Databricks Intelligence Platform (~10%): Understanding the architecture, components, and capabilities of the Databricks Data Intelligence Platform, including workspace navigation, cluster types (all-purpose vs. jobs), serverless compute, and the Lakehouse paradigm that unifies data warehousing and data lake functionality.
- 2.Development and Ingestion (~30%): Building data ingestion workflows using Auto Loader for incremental file ingestion, working with notebooks and Repos, reading from and writing to various data sources, and using Spark SQL and PySpark to extract and load data. Auto Loader configuration and schema inference/evolution are heavily tested topics.
- 3.Data Processing and Transformations (~31%): Applying the Medallion Architecture (Bronze/Silver/Gold layers), performing DDL and DML operations with Delta Lake, using Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) for declarative pipeline development, writing PySpark DataFrame transformations, implementing Liquid Clustering, and using higher-order functions and user-defined functions (UDFs).
- 4.Productionizing Data Pipelines (~18%): Orchestrating and scheduling multi-task jobs using Databricks Workflows, configuring failure recovery and alerting, managing deployments with Databricks Asset Bundles (DABs), monitoring pipeline performance, and leveraging serverless compute for production workloads.
- 5.Data Governance and Quality (~11%): Implementing access control and data lineage using Unity Catalog, managing metastores, catalogs, schemas, and tables, applying row/column-level security, using Delta Sharing for secure cross-platform data sharing, connecting to external data systems with Lakehouse Federation, and enforcing data quality constraints within pipelines.
Study Tips
- Download and study the official July 2025 Exam Guide PDF from databricks.com — it lists every testable objective with domain weights and is the single most authoritative preparation resource. Focus disproportionately on Development and Ingestion (30%) and Data Processing and Transformations (31%), as together they represent over 60% of the exam.
- Complete the free 'Data Engineer Learning Path' on Databricks Academy (academy.databricks.com). The self-paced courses 'Data Engineering with Databricks' and 'Advanced Data Engineering with Databricks' directly map to exam domains and include hands-on labs in a live Databricks environment.
- Gain hands-on practice with Auto Loader — configure schema inference, schema evolution (rescue data column), and checkpoint paths. Auto Loader is one of the most frequently tested topics in the Development and Ingestion domain, and understanding it operationally (not just conceptually) is critical.
- Practice writing and reading Delta Lake operations: MERGE INTO, OPTIMIZE with Liquid Clustering, VACUUM, table history (DESCRIBE HISTORY), time travel queries, and understanding when to use partitioning versus Liquid Clustering. These topics appear throughout the Data Processing domain.
- Build at least one end-to-end Lakeflow Declarative Pipeline (formerly Delta Live Tables) applying the Medallion Architecture. Understand the difference between streaming and batch tables, how to declare data quality expectations (CONSTRAINT clauses), and how pipeline modes (triggered vs. continuous) affect execution.
- Study Unity Catalog thoroughly — understand the three-level namespace (catalog.schema.table), how to grant privileges, the difference between managed and external tables, and how data lineage is captured. Although Unity Catalog carries only ~11% weight, it appears as context in questions across other domains.
- Take the official Databricks practice exam available at files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdf to calibrate your readiness, then review each incorrect answer against the official documentation to close knowledge gaps before exam day.
Career Benefits
Earning the Databricks Certified Data Engineer Associate credential demonstrates verified proficiency on one of the most widely adopted cloud data platforms, opening doors to roles such as Data Engineer, Analytics Engineer, ETL Developer, and Cloud Data Platform Engineer. As organizations increasingly migrate to Lakehouse architectures on Azure Databricks, AWS, and Google Cloud, employer demand for Databricks-certified professionals continues to grow. The certification is particularly valued at companies standardizing on the Databricks platform for their data and AI workloads.
From a compensation standpoint, Databricks-certified data engineers in the United States earn an average annual salary of approximately $129,716, with senior and specialized roles reaching $162,000 or more. The associate-level certification serves as both a standalone credential and a stepping stone to the Databricks Certified Data Engineer Professional exam, which covers advanced streaming, performance optimization, and testing patterns. Compared to general cloud certifications (such as AWS Data Analytics or Azure Data Engineer), this certification is highly specific to the Databricks ecosystem and is most valuable for professionals working in Databricks-centric environments.
Sample Questions
5 sample questions with answers and explanations. The full bank has 628 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A Lakeflow Job is scheduled with quartz_cron_expression set to 0 0 6 * * ? and timezone_id set to America/New_York. The job has max_concurrent_runs set to 1. A manual job run starts at 5:58 AM and runs until 6:05 AM EST. What happens to the scheduled 6:00 AM run? (Select one!)
AThe scheduled run queues and starts immediately when the manual run completes at 6:05 AM
BBoth runs execute concurrently with the manual run terminating early
CThe scheduled run is skipped because max_concurrent_runs is 1 and another run is active
DThe manual run is terminated at 6:00 AM to allow the scheduled run to start
Explanation
When max_concurrent_runs is 1, Databricks allows only one job run at a time. If a scheduled trigger fires while another run is active, the scheduled run is skipped entirely, not queued. The manual run started at 5:58 AM prevents the 6:00 AM scheduled run from starting. Databricks does not queue scheduled runs to execute later. To allow overlapping runs, increase max_concurrent_runs to 2 or higher. Manual runs are not automatically terminated for scheduled runs. Concurrent execution only occurs when max_concurrent_runs allows it.
2. A Lakeflow Job runs daily with max_concurrent_runs = 1. A job run starts at 09:00 but runs longer than expected. At 09:00 the next day, the schedule triggers a new run. What happens to the scheduled run? (Select one!)
AThe new run fails with a ConcurrentRunException error
BThe new run starts immediately, terminating the previous run
CThe new run is queued and waits for the current run to complete
DThe new run is skipped and the schedule continues the following day
Explanation
When max_concurrent_runs is set to 1 and a job is already running, new scheduled runs are skipped entirely rather than queued or failing. The job will attempt to run again on the next scheduled time. This prevents queue buildup for long-running jobs. The new run does not terminate the existing run, as that would disrupt in-progress work. Jobs are not automatically queued when max_concurrent_runs is reached for scheduled triggers. While manually triggered runs might fail or queue depending on configuration, scheduled runs are simply skipped when the concurrency limit is reached.
3. A Databricks Asset Bundle includes job definitions for development and production environments. The bundle configuration uses the variable ${bundle.target} to differentiate resource names. After running databricks bundle deploy -t prod, which command should be executed to trigger the deployed job? (Select one!)
Adatabricks workflows start my_job -t prod
Bdatabricks bundle execute my_job --target prod
Cdatabricks jobs run-now --job-name my_job-prod
Ddatabricks bundle run my_job -t prod
Explanation
The databricks bundle run command executes jobs defined in a deployed bundle, using the -t flag to specify the target environment. The jobs run-now command requires a numeric job-id, not a job name. The bundle execute command does not exist in the Databricks CLI. The workflows start command is not a valid Databricks CLI command.
4. A data platform team evaluates compute options for different workloads. The team needs to run ad-hoc SQL analytics queries from business intelligence tools, support concurrent users with automatic query queuing, and provide sub-second startup times. The solution must minimize infrastructure management overhead. Which compute type should they select? (Select one!)
AServerless SQL Warehouse with automatic scaling
BJob cluster with SQL endpoint integration
CAll-purpose cluster with autoscaling and automatic termination
DPro SQL Warehouse with multi-cluster scaling
Explanation
Serverless SQL Warehouses provide instant startup times in seconds, run in Databricks-managed compute accounts eliminating infrastructure management, include intelligent workload management for automatic query queuing, and support concurrent users with automatic scaling. Pro SQL Warehouses require approximately 4 minutes to start and run in customer cloud accounts. All-purpose clusters have restart times and higher DBU costs, and are not optimized for SQL analytics with BI tool integration. Job clusters are designed for batch processing not interactive SQL analytics and lack the SQL-specific optimizations and BI tool integrations.
5. A streaming application processes clickstream events using structured streaming with a 10-minute watermark on event_timestamp. Late events arriving up to 15 minutes after the watermark are being dropped. The business requires capturing all events arriving within 20 minutes. Which configuration change will meet this requirement? (Select one!)
AIncrease trigger interval to processingTime 20 minutes
BSet spark.sql.streaming.lateDataTolerance to 20 minutes
CChange withWatermark to withWatermark("event_timestamp", "20 minutes")
DChange output mode from append to update
Explanation
Watermark threshold directly controls how late data can arrive before being dropped from stateful aggregations. Changing the watermark from 10 minutes to 20 minutes allows events up to 20 minutes late to be processed. The configuration spark.sql.streaming.lateDataTolerance is not a valid Spark configuration property. Changing output mode does not affect late data handling. Trigger interval controls batch frequency but does not change watermark behavior or late data tolerance.
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