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 has delta.columnMapping.mode set to name with appropriate reader and writer versions configured. A data engineer needs to rename a column from customer_id to client_id and remove an unused column named legacy_field. Which SQL statements accomplish both tasks without rewriting the underlying Parquet files? (Select two!)
Multiple correct answersAALTER TABLE customers RENAME COLUMN customer_id TO client_id
BALTER TABLE customers DROP COLUMN legacy_field
CUPDATE customers SET client_id = customer_id
DALTER TABLE customers REPLACE COLUMNS (client_id INT, name STRING)
ECREATE TABLE customers_new AS SELECT customer_id AS client_id FROM customers
Explanation
Column mapping mode enables metadata-only operations for renaming and dropping columns without rewriting Parquet files. ALTER TABLE RENAME COLUMN updates the column name in the metadata mapping. ALTER TABLE DROP COLUMN marks the column as removed in the mapping without touching data files. UPDATE statements require reading and rewriting data. REPLACE COLUMNS performs a full schema replacement requiring data rewrite. CREATE TABLE AS SELECT creates a new table with a full data copy rather than modifying metadata.
2. A cluster pool is configured with min_idle_instances set to 5, max_capacity set to 20, and idle_instance_auto_termination_minutes set to 30. Currently, 3 instances are actively running clusters and 5 instances are idle in the pool. What charges apply to the 5 idle instances? (Select one!)
ACloud provider charges only, no DBU charges
BBoth DBU and cloud provider charges
CDBU charges only, no cloud provider charges
DNo charges for idle instances
Explanation
Idle instances in cluster pools incur cloud provider infrastructure charges but no DBU charges. Databricks only charges DBUs when instances are actively attached to running clusters. While idle instances consume cloud resources and generate cloud provider costs, they do not generate Databricks DBU costs. Both DBU and cloud provider charges would only apply to the 3 instances actively running clusters.
3. A data quality validation in a Lakeflow Declarative Pipeline defines the expectation CONSTRAINT valid_customer EXPECT (customer_id IS NOT NULL AND customer_id > 0) ON VIOLATION DROP ROW on a streaming table. During a pipeline run, approximately 15 percent of incoming records violate this expectation. Where can the data engineer view the count of dropped records and the specific expectation violation metrics? (Select one!)
AQuery the INFORMATION_SCHEMA.QUALITY_MONITORS view filtered by pipeline_id and table_name
BReview the Pipeline event log in the Lakeflow Pipelines UI showing data quality metrics per expectation
CQuery the system.lakeflow.pipeline_quality_metrics table with the pipeline name filter
DExecute DESCRIBE HISTORY on the target table to view dropped row counts in the operationMetrics column
Explanation
Lakeflow Declarative Pipelines provide data quality metrics including expectation violations, dropped rows, and failure counts in the Pipeline event log accessible through the Lakeflow Pipelines UI. The event log shows detailed metrics per expectation including the number of records that passed, failed, and were dropped. INFORMATION_SCHEMA.QUALITY_MONITORS is used for Lakehouse Monitoring quality checks on tables, not for Lakeflow pipeline expectations. DESCRIBE HISTORY shows Delta table operations and some metrics but does not provide detailed expectation-level statistics; it shows overall operation metrics but not per-expectation violation counts. There is no system.lakeflow.pipeline_quality_metrics table in Unity Catalog; pipeline metrics are accessed through the UI event log or via the DLT API.
4. A Delta table uses liquid clustering with CLUSTER BY (region, product_category). After analyzing query patterns, the team determines that adding customer_tier as a third clustering column would improve performance. They also need to remove product_category from clustering. What is the correct approach? (Select one!)
ARun OPTIMIZE sales CLUSTER BY (region, customer_tier) to change clustering columns
BALTER TABLE sales CLUSTER BY (region, customer_tier); Run OPTIMIZE to apply changes
CALTER TABLE sales DROP CLUSTERING ON (product_category); ALTER TABLE sales ADD CLUSTERING ON (customer_tier); Run OPTIMIZE
DLiquid clustering columns cannot be changed once set; recreate the table with new clustering definition
Explanation
Liquid clustering allows changing clustering columns without rewriting the entire table by using ALTER TABLE CLUSTER BY with the new column list. Running OPTIMIZE afterward applies the new clustering incrementally to data files. This is a key advantage over Z-ORDER, which requires running OPTIMIZE ZORDER BY each time and does not persist the column configuration. There are no separate DROP CLUSTERING or ADD CLUSTERING commands for liquid clustering—you simply specify the complete new column list. OPTIMIZE alone cannot change clustering column definitions; it only applies the clustering defined by the table properties. Unlike Z-ORDER, liquid clustering columns can be changed at any time without recreating the table, with a maximum of 4 clustering columns.
5. A data engineering team runs VACUUM on a production Delta table every week with default settings. After the latest VACUUM operation, users report they can no longer query the table using VERSION AS OF from 10 days ago, even though the delta.logRetentionDuration is set to 30 days. What is the most likely cause? (Select one!)
AThe VACUUM operation requires delta.deletedFileRetentionDuration to match delta.logRetentionDuration
BThe table checkpoint files were corrupted during the VACUUM operation
CThe transaction log files were deleted because delta.logRetentionDuration expired
DVACUUM removed the data files needed for time travel even though transaction logs still exist
Explanation
VACUUM removes data files that are no longer referenced by the Delta table based on delta.deletedFileRetentionDuration, which defaults to 7 days. Even though transaction logs are retained for 30 days by default, time travel requires BOTH the transaction log entries AND the actual data files. After VACUUM runs with the default 7-day retention, data files older than 7 days are permanently deleted, making time travel beyond that point impossible. The transaction log files still exist but point to deleted data files. Checkpoint files are not deleted by VACUUM. The two retention properties serve different purposes and do not need to match, though setting deletedFileRetentionDuration lower than logRetentionDuration limits practical time travel capability.
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