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
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
Preview — answers shown5 sample questions with correct answers and explanations. Start a practice session to test yourself across all 628 questions.
1. A data engineer analyzes a slow join operation in the Spark UI and observes that the Exchange operator appears multiple times in the physical plan with high shuffle read and shuffle write metrics. The Stages tab shows some tasks have shuffle read sizes of 5 GB while most tasks read only 500 MB. What issue is indicated by these symptoms? (Select one!)
ACheckpoint corruption requiring stream restart
BData skew causing uneven partition distribution across tasks
CBroadcast join threshold is set too high for available memory
DInsufficient executor memory causing excessive garbage collection
Explanation
Data skew is indicated when maximum task duration or data size is significantly larger than the median or 75th percentile, as shown by some tasks reading 5 GB while most read only 500 MB. Multiple Exchange operators indicate shuffle operations, and uneven shuffle read sizes across tasks is the classic symptom of skewed data distribution where some partitions have much more data than others. Broadcast join threshold being too high would cause memory pressure but would eliminate Exchange operators by broadcasting the smaller dataset instead of shuffling. Insufficient executor memory would show high GC time in the Tasks tab, not uneven shuffle sizes. Checkpoint corruption is unrelated to Spark UI metrics for shuffle operations and would manifest as streaming job failures.
2. A healthcare analytics team queries a patient_visits table partitioned by visit_date. The table receives daily updates that overwrite data for the current date. The data engineer uses the following configuration: SET spark.sql.sources.partitionOverwriteMode = dynamic; INSERT OVERWRITE TABLE patient_visits SELECT * FROM daily_updates WHERE visit_date = '2024-01-15'; What is the effect of the dynamic partition overwrite mode? (Select one!)
AAll partitions for January 2024 are overwritten
BOnly the partition for visit_date = '2024-01-15' is overwritten; all other partitions remain unchanged
CThe entire table is overwritten with data from daily_updates
DThe operation fails because INSERT OVERWRITE requires static partition overwrite mode
Explanation
Dynamic partition overwrite mode only overwrites partitions that have data in the source DataFrame. When the source contains only visit_date = '2024-01-15', only that specific partition is replaced while all other date partitions remain untouched. This is ideal for daily incremental updates. Without dynamic mode (using static mode), INSERT OVERWRITE would replace the entire table. The partition scope is determined by the actual data in the source, not by date ranges like the entire month. INSERT OVERWRITE works with both static and dynamic modes.
3. A Unity Catalog administrator needs to grant a data_analysts group the ability to query tables in the sales.customers schema but not create new tables. Which privileges must be granted? (Select two!)
Multiple correct answersAGRANT USE CATALOG ON CATALOG sales TO data_analysts
BGRANT USE SCHEMA ON SCHEMA sales.customers TO data_analysts
CGRANT SELECT ON SCHEMA sales.customers TO data_analysts
DGRANT CREATE TABLE ON SCHEMA sales.customers TO data_analysts
EGRANT ALL PRIVILEGES ON CATALOG sales TO data_analysts
Explanation
Unity Catalog requires cascading USE privileges for users to access objects. Users must have both USE CATALOG on the catalog and USE SCHEMA on the schema to access tables. However, SELECT privilege can be granted at the schema level, which applies to all existing and future tables in that schema. Granting USE SCHEMA alone is insufficient without USE CATALOG. Schema-level SELECT grants read access to all tables without granting CREATE TABLE privileges. Granting CREATE TABLE would violate the requirement. ALL PRIVILEGES includes CREATE SCHEMA and other administrative privileges beyond what is needed.
4. A data team uses COPY INTO to incrementally load CSV files from an S3 bucket into a Delta table. After the initial successful load, new files are added to the bucket but COPY INTO reports zero files loaded even though the files exist. Which COPY_OPTIONS setting would force reloading of all files? (Select one!)
AAdd PATTERN option to match the new file names
BSet force to true in COPY_OPTIONS
CSet mergeSchema to true in COPY_OPTIONS
DUse FORMAT_OPTIONS with inferSchema set to true
Explanation
Setting force to true in COPY_OPTIONS disables the idempotent behavior of COPY INTO, causing it to reload all matched files regardless of whether they were previously processed. This is tracked in the Delta transaction log. The mergeSchema option allows schema evolution when new columns appear but does not affect which files are processed. The PATTERN option filters which files are considered but doesn't override the idempotency tracking that prevents reprocessing. The inferSchema FORMAT_OPTIONS setting controls schema inference from file contents but does not affect file selection or reprocessing behavior.
5. A production job occasionally fails due to transient network errors when reading from external APIs. The data engineer configures retry policies with max_retries set to 3 and min_retry_interval_millis set to 60000. On the second retry attempt, how long does Databricks wait before retrying the failed task? (Select one!)
AExactly 60 seconds as specified by min_retry_interval_millis
BNo wait time because min_retry_interval_millis only applies to the first retry
CAt least 60 seconds with exponential backoff applied
D30 seconds due to exponential backoff reduction
Explanation
The min_retry_interval_millis parameter specifies the minimum time to wait before retrying, but Databricks applies exponential backoff to subsequent retries. On the first retry, the wait is at least 60 seconds. On the second retry, exponential backoff increases the wait time beyond the minimum. The parameter establishes a floor, not an exact duration, so the actual wait time will be at least 60 seconds but potentially longer based on exponential backoff calculations. The setting applies to all retries, not just the first one, and backoff increases wait times rather than decreasing them.
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