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 Delta table named transactions has the following table properties configured: delta.logRetentionDuration = 30 days and delta.deletedFileRetentionDuration = 7 days. A data engineer runs VACUUM transactions RETAIN 168 HOURS on day 10. On day 15, they attempt to query the table at a version from day 3 using time travel. What is the expected outcome? (Select one!)
AThe query succeeds because the 168-hour retention in VACUUM overrides the table property
BThe query succeeds because transaction logs are retained for 30 days
CThe query fails because time travel is limited to the delta.logRetentionDuration period
DThe query fails because VACUUM removed data files older than 7 days on day 10
Explanation
Time travel requires both transaction logs AND data files to reconstruct historical table states. Although transaction logs are retained for 30 days, VACUUM with RETAIN 168 HOURS (7 days) run on day 10 would have removed data files that were no longer referenced and older than 7 days from that point. Data files from day 3 would be 7 days old on day 10 and subject to deletion. By day 15, those files would be gone for 5 days, making time travel to day 3 impossible. The 168-hour retention in VACUUM does not override the deletion that already occurred. Log retention alone is insufficient for time travel functionality.
2. A production Delta table has the following retention properties configured: delta.logRetentionDuration set to 30 days and delta.deletedFileRetentionDuration set to 7 days. VACUUM runs daily with default settings. A data analyst needs to query the table as it existed 15 days ago. What will happen? (Select one!)
AThe query fails because the default VACUUM retention is 30 days
BThe query fails because VACUUM removed data files after 7 days
CThe query succeeds because transaction logs are retained for 30 days
DThe query succeeds because both retention settings allow 15-day time travel
Explanation
Time travel requires both transaction log metadata and the actual data files to reconstruct historical table states. While delta.logRetentionDuration of 30 days preserves the transaction log entries needed to identify which files comprised the table 15 days ago, delta.deletedFileRetentionDuration controls how long VACUUM retains data files before deletion. With daily VACUUM runs using the default 7-day retention, data files older than 7 days are removed, making 15-day time travel impossible even though the logs exist. Practical time travel capability is limited by the shorter of the two retention periods. The default VACUUM retention is indeed 7 days (168 hours), not 30 days, which is the source of the limitation.
3. A data team loads CSV files using COPY INTO with the following command: COPY INTO sales_data FROM 's3://bucket/sales/' FILEFORMAT = CSV. After the initial load completes successfully, new CSV files are added to the same S3 location. When the team runs the same COPY INTO command again, the new files are not loaded. What is the cause? (Select one!)
ACOPY INTO tracks loaded files in the Delta transaction log and skips them by default
BCOPY INTO only works for initial loads and requires Auto Loader for incremental processing
CCOPY INTO requires the PATTERN option to identify new files
DThe mergeSchema option must be set to true for incremental loads
Explanation
COPY INTO provides idempotent behavior by tracking successfully loaded files in the Delta transaction log and automatically skipping them on subsequent runs. This prevents duplicate data loading. To reload files, the force option must be set to true in COPY_OPTIONS. The PATTERN option filters which files to process but does not affect the idempotency behavior. The mergeSchema option controls schema evolution, not incremental loading behavior. While Auto Loader is recommended for continuous incremental processing of millions of files, COPY INTO does support incremental loads for thousands of files through its built-in tracking mechanism.
4. A data governance team needs to implement row-level security on a Unity Catalog table sales_transactions. Users in the finance group should see all rows, while users in the regional_managers group should only see rows for their assigned region stored in the region column. The user's assigned region is stored in a separate table user_regions. Which approach implements this requirement? (Select one!)
ACreate a row filter function that joins with user_regions table and apply it using ALTER TABLE SET ROW FILTER
BUse a view with a WHERE clause filtering by current_user() and grant access to the view instead of the table
CCreate a column mask on the region column that returns NULL for unauthorized users
DCreate separate tables for each region and grant appropriate SELECT privileges
Explanation
Creating a row filter function that uses is_account_group_member to check group membership and joins with the user_regions table to verify the user's assigned region, then applying it with ALTER TABLE SET ROW FILTER, is the correct Unity Catalog approach for row-level security. Row filters are evaluated at query time and transparently filter rows based on the current user's identity. Column masks modify column values but do not filter entire rows, so masking the region column would still return all rows. Using a view with WHERE clause filtering is a valid alternative but requires managing separate view permissions and does not provide the same transparent security as built-in row filters. Creating separate tables for each region creates significant administrative overhead and does not scale well.
5. A Unity Catalog administrator needs to implement column-level security on the employees table to mask Social Security Numbers. Users in the hr_team group should see the full SSN, while all other users should see XXX-XX-XXXX. The SSN column is named social_security_number. Which approach should the administrator use? (Select one!)
AALTER TABLE employees ADD CONSTRAINT ssn_mask CHECK (is_account_group_member('hr_team'))
BGRANT SELECT(social_security_number) ON TABLE employees TO hr_team; REVOKE SELECT(social_security_number) FROM all_users
CCREATE VIEW employees_masked AS SELECT CASE WHEN is_member('hr_team') THEN social_security_number ELSE 'XXX-XX-XXXX' END FROM employees
DCREATE FUNCTION mask_ssn(ssn STRING) RETURNS STRING RETURN CASE WHEN is_account_group_member('hr_team') THEN ssn ELSE 'XXX-XX-XXXX' END; then ALTER TABLE employees ALTER COLUMN social_security_number SET MASK mask_ssn
Explanation
Creating a SQL UDF that uses is_account_group_member to check group membership and applying it as a column mask with ALTER TABLE ALTER COLUMN SET MASK is the correct Unity Catalog approach for column-level security. This dynamically masks values at query time based on user identity. Creating a view does not provide true column masking and uses the deprecated is_member function for workspace-level groups instead of Unity Catalog account groups. Column-level grants do not support conditional masking based on values. CHECK constraints are for data validation, not access control or masking.
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