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 streaming pipeline processes clickstream events and needs to calculate the number of unique users per 5-minute window. Events can arrive up to 15 minutes late due to network delays. The pipeline must prevent unbounded state growth while maintaining accuracy for late events. Which combination of Structured Streaming features should be implemented? (Select two!)
Multiple correct answersAUse withWatermark with 15 minutes threshold before the groupBy operation
BConfigure the stream with trigger mode processingTime of 5 minutes
CUse window function with 5-minute duration in the groupBy clause
DSet outputMode to complete to ensure all windows are recalculated
EEnable checkpointing with a retention period of 15 minutes
Explanation
Watermarking with a 15-minute threshold allows the pipeline to accept late events arriving within 15 minutes of the event timestamp, while also enabling Spark to drop state for windows older than the watermark threshold, preventing unbounded state growth. The window function with 5-minute duration creates tumbling windows for aggregation. Using processingTime trigger controls batch frequency but doesn't address late data or state management. Complete output mode would rewrite entire results on each trigger, which is inefficient and doesn't properly handle late data in windowed aggregations. Checkpoint retention controls fault tolerance but doesn't manage watermarking or state cleanup.
2. A cluster administrator creates a cluster pool with the following configuration: min_idle_instances = 3, max_capacity = 10, idle_instance_auto_termination_minutes = 30. A data engineering team uses this pool for job clusters. During peak hours, 8 clusters are running. After all jobs complete, how many instances remain in the pool after 30 minutes, and what are the cost implications? (Select one!)
A8 instances remain in the pool until manually terminated; both cloud provider and DBU charges continue
B0 instances remain because all instances terminate after the idle timeout period expires
C3 instances remain idle in the pool; cloud provider compute charges apply but no DBU charges are incurred for idle instances
D3 instances remain idle in the pool; both cloud provider compute charges and DBU charges apply to idle instances
Explanation
Cluster pools maintain a minimum number of idle instances specified by min_idle_instances. After 30 minutes of idle time, instances beyond the minimum are terminated, but the pool keeps 3 instances ready to reduce cluster startup time. Idle instances in a pool incur cloud provider compute charges because the VMs remain running, but Databricks does not charge DBUs while instances are idle in the pool. DBU charges only apply when instances are attached to active clusters. The pool does not terminate all instances because min_idle_instances ensures 3 remain available. Instances above the minimum are automatically terminated after the idle timeout.
3. A data engineer optimizes storage costs for a historical orders table with 5 years of data. The table has the following properties: delta.logRetentionDuration = 30 days delta.deletedFileRetentionDuration = 7 days VACUUM was last run 10 days ago. A business analyst needs to query data from 15 days ago using time travel. What will be the result? (Select one!)
AThe query will succeed if run within the next 5 days, after which the data will be permanently deleted
BThe query will return partial results with some files missing
CThe query will fail because VACUUM removed data files older than 7 days retention threshold
DThe query will succeed because the transaction log retains 30 days of history
Explanation
Time travel requires both transaction log history and the actual data files. The transaction log retains 30 days of history, but VACUUM removes data files older than the deletedFileRetentionDuration threshold (7 days). Since VACUUM was run 10 days ago, it removed files older than 7 days from that point. Data from 15 days ago still has 5 days remaining before the next VACUUM would remove those files (17 days total from last VACUUM minus 10 days already passed equals 7 days remaining). The query will succeed now but fail after the 7-day retention window expires. Partial results would indicate corruption, not expected VACUUM behavior.
4. A data pipeline uses a Lakeflow Declarative Pipeline with the following SQL definition: CREATE STREAMING TABLE bronze_events ( CONSTRAINT valid_timestamp EXPECT (event_timestamp IS NOT NULL) ON VIOLATION DROP ROW, CONSTRAINT future_check EXPECT (event_timestamp <= current_timestamp()) ) AS SELECT * FROM cloud_files('/data/events/', 'json'); After deployment, the pipeline processes data but the data quality metrics show that some records with future timestamps are being ingested. What is the most likely cause? (Select one!)
AStreaming tables do not support multiple expectations, causing the second constraint to be ignored
BExpectations are only enforced in materialized views, not streaming tables
CThe future_check expectation has no ON VIOLATION clause, so it only logs violations without dropping rows
DThe current_timestamp() function is evaluated at pipeline creation time, not at record processing time
Explanation
In Lakeflow Declarative Pipelines, expectations without an ON VIOLATION clause default to the warn behavior, which logs the violation in the event log but continues processing the record. The future_check expectation lacks an ON VIOLATION clause, so records with future timestamps are logged as violations but still ingested into the table. To drop these rows, the constraint should include ON VIOLATION DROP ROW. Streaming tables fully support multiple expectations. The current_timestamp() function is evaluated at processing time for each micro-batch. Expectations are enforced in both streaming tables and materialized views.
5. A Lakeflow Declarative Pipeline processes transaction data through bronze, silver, and gold layers. The pipeline definition includes: CREATE STREAMING TABLE bronze_txn AS SELECT * FROM cloud_files('/data/raw/', 'json'); CREATE MATERIALIZED VIEW silver_txn AS SELECT txn_id, amount, DATE(timestamp) as txn_date FROM bronze_txn WHERE amount > 0; CREATE MATERIALIZED VIEW gold_daily_summary AS SELECT txn_date, SUM(amount) as total FROM silver_txn GROUP BY txn_date;. The pipeline runs successfully, but users report that the gold_daily_summary table does not reflect transactions that arrived in the last hour. What is the cause of this data latency? (Select one!)
AThe silver_txn materialized view performs a full refresh batch operation that introduces latency
BMaterialized views only refresh on manual REFRESH MATERIALIZED VIEW commands or pipeline updates
CThe WHERE clause in silver_txn filters out recent transactions with timestamps in the future
DThe bronze_txn streaming table has not triggered a micro-batch in the last hour due to trigger configuration
Explanation
Materialized views in Lakeflow Declarative Pipelines perform full refresh operations rather than incremental processing. When silver_txn is defined as a materialized view reading from a streaming table, it does not incrementally process new data but instead re-reads the entire bronze_txn table on each refresh, introducing latency. The correct design for near real-time data flow is to use streaming tables for bronze and silver layers and reserve materialized views for gold layer aggregations. Bronze streaming tables continuously process new files with minimal latency. Materialized views in DLT pipelines refresh automatically during pipeline updates, not only on manual commands. The WHERE clause filters negative amounts, not future timestamps.
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