Databricks · DCDEA
Databricks Certified Data Engineer Associate Practice Test
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
Questions
628
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. Start a practice session to test yourself across all 628 questions.
Preview — answers shown1. A data engineering team orchestrates a complex ETL workflow using dbutils.notebook.run(). The child notebook processes customer records and needs to return detailed statistics including record counts, error messages, and validation results totaling approximately 8 MB of data. The team reports that the parent notebook is not receiving complete results. What is the MOST appropriate solution? (Select one!)
AIncrease the timeout parameter in dbutils.notebook.run() to allow larger returns
BSerialize the statistics as JSON, write to a Delta table, and return the table path
CStore statistics in a global temporary view and return the view name to the parent notebook
DSplit the child notebook into multiple notebooks, each returning under 5 MB
Explanation
Global temporary views allow notebooks in the same cluster to share data without size limitations since they reside in the same JVM. The child notebook creates the view with statistics, returns only the view name (well under 5 MB), and the parent queries the view. Writing to a Delta table works but adds unnecessary I/O overhead for temporary statistics. Splitting notebooks adds orchestration complexity without addressing the root cause. The timeout parameter controls execution time, not return value size, which has a 5 MB limit in dbutils.notebook.run().
2. A streaming query uses foreachBatch to implement custom MERGE logic into a Delta table. The engineer observes duplicate records in the target table after a streaming job failure and restart. What is the most likely cause? (Select one!)
AThe checkpoint location was deleted before the restart
BMERGE operations in foreachBatch are not idempotent by default
CThe foreachBatch function does not use the batch_id parameter for idempotent writes
DThe streaming query is missing the trigger(availableNow=True) option
Explanation
When using foreachBatch with MERGE operations, the batch_id parameter must be used to ensure idempotent writes. After a failure, Structured Streaming may reprocess the last incomplete micro-batch. If the foreachBatch function does not track which batches were successfully committed using batch_id, it may reprocess and insert duplicate records. Proper implementation writes batch_id to a separate tracking table or uses it in merge conditions to prevent reprocessing. A deleted checkpoint would cause the stream to restart from the beginning, not just create duplicates. MERGE operations are idempotent when properly implemented with batch tracking. The trigger type does not affect idempotency.
3. A Lakehouse Federation setup connects a Databricks workspace to an external PostgreSQL database. The data engineering team creates a foreign catalog and queries tables successfully. However, when they try to create a view that joins a foreign PostgreSQL table with a Unity Catalog Delta table, the query returns an error about mixed catalog sources. What is the cause of this limitation? (Select one!)
AForeign catalogs must be refreshed using ALTER FOREIGN CATALOG REFRESH before joining with Delta tables
BLakehouse Federation only supports read queries on foreign tables and cannot join them with Unity Catalog tables in a single query
CThe PostgreSQL connection requires additional permissions to enable cross-catalog joins
DThe Unity Catalog Delta table must be converted to an external table to join with foreign tables
Explanation
Lakehouse Federation has important limitations: it provides read-only access to external databases and queries must execute entirely against either the foreign catalog or Unity Catalog tables, but cannot join across catalog boundaries in a single query. This is because the query execution cannot span multiple database systems with different query engines. To work with both data sources, users must read data from the foreign catalog into a Unity Catalog table first, then perform joins. Additional permissions on the PostgreSQL connection do not enable cross-catalog joins. There is no ALTER FOREIGN CATALOG REFRESH command. Converting Unity Catalog tables to external tables does not remove the fundamental limitation of cross-catalog query execution.
4. A cluster administrator creates a cluster pool with min_idle_instances set to 5 and max_capacity set to 20. The pool has been running for 24 hours with varying usage. During a period when 15 instances are allocated to active clusters, what is the total cost for the pool? (Select one!)
ADBU costs for 15 instances plus cloud provider costs for 15 instances
BDBU costs for 15 instances plus cloud provider costs for 20 instances
CNo DBU costs for idle instances plus cloud provider costs for 20 instances
DDBU costs for 5 idle instances plus cloud provider costs for 15 active instances
Explanation
Cluster pools do not charge DBUs for idle instances, only for instances actively used by clusters. When 15 instances are allocated to active clusters, DBU charges apply only to those 15 instances. Cloud provider charges apply only to instances that are running, which includes the 15 active instances. The pool does not maintain instances beyond those in use unless they are idle from recent terminations within the auto-termination window. The max_capacity setting limits total instances but does not cause all 20 instances to run continuously.
5. A development team uses %run to execute a utility notebook containing user-defined functions. The main notebook and utility notebook are stored in different directories. The %run command fails with a path not found error. What is the most likely cause? (Select one!)
AThe %run command only supports relative paths using dot notation
BThe %run command requires absolute paths starting with /Workspace/ or /Repos/
CCross-directory notebook execution requires configuring notebook dependencies in job settings
DUtility notebooks must be in the same directory as the calling notebook
Explanation
The %run magic command requires absolute paths starting with /Workspace/ for workspace notebooks or /Repos/ for notebooks in Databricks Repos. Relative paths are not supported. Utility notebooks do not need to be in the same directory as long as the absolute path is correctly specified. Dot notation for relative paths is not supported in %run commands. Notebook dependencies are not configured in job settings; %run works based on path resolution.
More Databricks Practice Exams
Databricks Certified Machine Learning Associate
DCMLEA · 630 questions
Databricks Certified Data Engineer Professional
DCDEP · 628 questions
Databricks Certified Data Analyst Associate
DCDAA · 627 questions
Databricks Certified Machine Learning Professional
DCMLEP · 622 questions
Databricks Certified Generative AI Engineer Associate
DCGAE · 620 questions
Databricks Certified Associate Developer for Apache Spark
DCASD · 604 questions
$17.99
One-time access to this exam
Full access to all 628 questions
Or $15/mo for all 253 exams
Detailed explanations
Free preview stays available