Databricks Certified Data Engineer Associate (Data Engineer Associate) practice exam and study path
A free, timed 45-question Data Engineer Associate practice exam in 90 minutes, scored by domain, with every option explained after you submit. Also 177 practice questions you can check one at a time, a page for every task in the exam guide, and a study path. No sign-up.
$6.99for 12 months
The foundational Databricks data engineering exam: the platform and its compute, ingesting with COPY INTO, Auto Loader and Lakeflow Connect, transforming data with PySpark and SQL into silver and gold tables, orchestrating with Lakeflow Jobs, shipping with Git folders and Declarative Automation Bundles, troubleshooting performance, and governing data with Unity Catalog.
The exam
- Exam code
- Data Engineer Associate
- Questions
- Not yet published
- Time
- 90 minutes
- Passing score
- Not published
- Exam fee
- $200 (USD)
- Question types
- multiple choice, multiple response
45 scored multiple-choice questions in 90 minutes (unscored items may be added and are not identified), USD 200 plus applicable taxes, online or at a test center, no test aids, in English, Japanese, Portuguese (BR) and Korean. No prerequisite; related training and six months of hands-on Databricks experience are recommended. Valid for 2 years; to recertify you take the current exam. Passing score: Not published. This outline is the version live since May 4, 2026.
From Databricks's certification page and exam guide, checked Oct 19, 2026.
Domains and weights
Domain 1: Databricks Intelligence Platform
6%
Domain 2: Data Ingestion and Loading
21%
Domain 3: Data Transformation and Modeling
22%
Domain 4: Working with Lakeflow Jobs
16%
Domain 5: Implementing CI/CD
10%
Domain 6: Troubleshooting, Monitoring, and Optimization
10%
Domain 7: Governance and Security
15%
Practice
177 original practice questions, every option explained with the Databricks documentation page that proves it. Free, no sign-up; progress stays on this device.
177 questions · practice mode
All domains
Every sample question for this exam, in domain order.
11 questions · practice mode
Topic quiz — Domain 1: Databricks Intelligence Platform
A topic quiz on domain 1 (6% of the exam).
37 questions · practice mode
Topic quiz — Domain 2: Data Ingestion and Loading
A topic quiz on domain 2 (21% of the exam).
38 questions · practice mode
Topic quiz — Domain 3: Data Transformation and Modeling
A topic quiz on domain 3 (22% of the exam).
23 questions · practice mode
Topic quiz — Domain 4: Working with Lakeflow Jobs
A topic quiz on domain 4 (16% of the exam).
21 questions · practice mode
Topic quiz — Domain 5: Implementing CI/CD
A topic quiz on domain 5 (10% of the exam).
25 questions · practice mode
Topic quiz — Domain 6: Troubleshooting, Monitoring, and Optimization
A topic quiz on domain 6 (10% of the exam).
22 questions · practice mode
Topic quiz — Domain 7: Governance and Security
A topic quiz on domain 7 (15% of the exam).
45 questions · 90 min · timed
Full practice exam
The real exam's pace, no feedback until you submit.
45 questions · 90 min · timed · exam pack
Practice exam 1
Included in the Data Engineer Associate exam pack.
45 questions · 90 min · timed · exam pack
Practice exam 2
Included in the Data Engineer Associate exam pack.
45 questions · 90 min · timed · exam pack
Practice exam 3
Included in the Data Engineer Associate exam pack.
More practice exams
The lessons, the sample questions on every task page and the first full practice exam are free and stay free. A pack adds the further timed Data Engineer Associate practice exams.
Data Engineer Associate exam pack
$6.99for 12 months
- Every paid Data Engineer Associate practice exam, timed, with every option explained
- The next version of this exam included when it changes within your 12 months
- 12 months of access. One payment, nothing renews by itself.
- 14-day refund, no conditions
- 14-day refund, no conditions: ask within 14 days of buying and you get the whole payment back.
- No pass guarantee. These are practice exams written by us from the exam guide and the Databricks documentation; a score here does not predict your result on the real exam.
Study path
One module per exam domain. Lessons that already exist on BytePatterns are linked; the rest are being written.
Module 1: Databricks Intelligence Platform
1Control plane, compute plane, workspaces and the Unity Catalog metastoreTask 1.1
Lesson coming
2Delta Lake: the transaction log, ACID writes, history and time travelTask 1.1
Partly covered by: Transactions & ACID
3Unity Catalog: the three-level namespace and securable objectsTask 1.1
Lesson coming
4Choosing compute: serverless, classic jobs and all-purpose compute, SQL warehousesTask 1.2
Partly covered by: Vertical vs Horizontal Scaling
5Access modes, pools, Photon, and what each compute type costsTask 1.2
Lesson coming
Module 2: Data Ingestion and Loading
6Batch, streaming and incremental ingestion; triggered and continuous pipelinesTask 2.1
Partly covered by: Message Queues
7Uploading local files to tables and volumesTask 2.1
Lesson coming
8COPY INTO: idempotent loads, options, validation and privilegesTask 2.2
Lesson coming
9Auto Loader: schema inference, evolution modes and the rescued data columnTask 2.3
Lesson coming
10Auto Loader: directory listing, file notification and file eventsTask 2.3
Lesson coming
11Lakeflow Connect managed connectors: connections, gateways, pipelines and schedulesTask 2.4
Lesson coming
12JDBC reads and REST calls from a notebook, scheduled as a jobTask 2.5
Partly covered by: Designing a REST API
13Choosing an ingestion method: managed connectors, Auto Loader, COPY INTO, partners, federationTask 2.6
Lesson coming
14JSON, nested data and VARIANT; unstructured filesTask 2.7
Lesson coming
Module 3: Data Transformation and Modeling
15Cleaning bronze into silver: nulls, types and standard valuesTask 3.1
Lesson coming
16Joins, broadcast joins, cross joins, union and union allTask 3.2
Partly covered by: INNER and OUTER JOINs
17Adding, dropping, splitting and renaming columns; filters; explodeTask 3.3
Lesson coming
18Deduplication and aggregation: dropDuplicates, groupBy, approx_count_distinct, summaryTask 3.4
Partly covered by: Aggregations & GROUP BY, Window Functions
19Spark tuning parameters and measuring againTask 3.5
Partly covered by: Reading a Query Plan
20Gold objects: views, materialized views, streaming tables and tablesTask 3.6
Lesson coming
21Data quality: expectations, constraints and quarantineTask 3.7
Lesson coming
Module 4: Working with Lakeflow Jobs
22Retries, If/else, Run if and For each tasksTask 4.1
Partly covered by: Design a Job Scheduler
23Task types, dependencies, parameters and task valuesTask 4.2
Lesson coming
24Schedules, file arrival triggers, table update triggers and continuous jobsTask 4.3
Lesson coming
25Time-based or data-driven: picking the triggerTask 4.4
Lesson coming
Module 5: Implementing CI/CD
26Git folders: clone, branch, commit, push, pull and mergeTask 5.1
Lesson coming
27Bundle variables, substitutions and target overridesTask 5.2
Lesson coming
28Deploying bundles: resources, targets, modes and run_asTask 5.3
Lesson coming
29The Databricks CLI in a CI/CD workflowTask 5.4
Lesson coming
Module 6: Troubleshooting, Monitoring, and Optimization
30Run history, the matrix view and job system tablesTask 6.1
Partly covered by: Observability Basics
31Job and task states, run graphs, notifications and duration alertsTask 6.2
Partly covered by: Observability Basics
32Reading the Spark UI: skew, shuffle and spillTask 6.3
Partly covered by: Database Sharding
33Liquid clustering and predictive optimizationTask 6.4
Lesson coming
34Cluster start failures, library conflicts and out-of-memory errorsTask 6.5
Lesson coming
Module 7: Governance and Security
35Managed and external tables: create, alter, drop, undrop and convertTask 7.1
Lesson coming
36Privileges: GRANT, REVOKE, inheritance, ownership and denyTask 7.2
Partly covered by: Shared Responsibility & IAM
37Row filters, column masks and dynamic viewsTask 7.3
Lesson coming
38ABAC policies with governed tagsTask 7.4
Lesson coming
Every task in the exam guide
The exam guide splits each domain into task statements. Each one has a page with a short summary, the lessons that teach it and open sample questions.
Domain 1: Databricks Intelligence Platform
- 1.1Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.5 sample questions
- 1.2Understand Databricks Data Intelligence Platform's compute services, including their characteristics, limitations, and cost models, and select the most suitable option for each workload use case.6 sample questions
Domain 2: Data Ingestion and Loading
- 2.1Enable and detail data ingestion patterns, including batch, streaming, and incremental loading, and import data from sources such as local files, Lakeflow Connect standard connectors, and Lakeflow Connect managed connectors.6 sample questions
- 2.2Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity Catalog–governed tables.5 sample questions
- 2.3Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into Unity Catalog–governed tables.6 sample questions
- 2.4Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into Unity Catalog–governed tables.5 sample questions
- 2.5Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into Unity Catalog–governed tables, usually orchestrated and scheduled with Lakeflow Jobs.5 sample questions
- 2.6Prioritize between Auto Loader, Lakeflow Connect (standard and managed connectors), partner connectors, and other ingestion methods based on technical requirements such as data volume, ingestion frequency, data types, and governance needs with Unity Catalog.5 sample questions
- 2.7Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into Unity Catalog–governed Delta tables.5 sample questions
Domain 3: Data Transformation and Modeling
- 3.1Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.6 sample questions
- 3.2Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.6 sample questions
- 3.3Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.5 sample questions
- 3.4Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.5 sample questions
- 3.5Understand the basic tuning parameters (spark.sql.shuffle.partitions, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.5 sample questions
- 3.6Understand the difference between, and how to build, Gold layer objects such as materialized views, views, streaming tables, and tables for BI and analytics teams in Unity Catalog.6 sample questions
- 3.7Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.5 sample questions
Domain 4: Working with Lakeflow Jobs
- 4.1Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration.6 sample questions
- 4.2Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG-based task graph.6 sample questions
- 4.3Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update).6 sample questions
- 4.4Choose between time-based and data-driven triggers based on data availability and pipeline dependencies.5 sample questions
Domain 5: Implementing CI/CD
- 5.1Manage your code development workflow within the Databricks workspace UI, including creating and switching between branches in Databricks Git Folders (formerly Databricks Repos), committing and pushing changes, and creating pull requests using Databricks Git integration.5 sample questions
- 5.2Understand environment-specific configuration using Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets.5 sample questions
- 5.3Deploy Declarative Automation Bundles (formerly Databricks Asset Bundles) to package, configure, and promote Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, and other workspace assets across dev, test, and prod environments.6 sample questions
- 5.4Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows.5 sample questions
Domain 6: Troubleshooting, Monitoring, and Optimization
- 6.1Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.5 sample questions
- 6.2Use the Lakeflow Jobs UI to monitor pipeline health by interpreting job statuses, viewing DAG-based task graphs to spot upstream blockers, and tracking pipeline run times and failure rates.5 sample questions
- 6.3Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.5 sample questions
- 6.4Understand the features of Liquid Clustering and predictive optimization.5 sample questions
- 6.5Diagnose cluster startup failures, library conflicts, and out-of-memory issues.5 sample questions
Domain 7: Governance and Security
- 7.1Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them.6 sample questions
- 7.2Configure access controls using the UI and SQL by applying GRANT, REVOKE, and DENY privileges to principals (users, groups, and service principals) at appropriate levels of the security hierarchy.6 sample questions
- 7.3Understand column-level masking and row-level security to restrict data visibility based on user groups.5 sample questions
- 7.4Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.5 sample questions
How these questions are made
- Written by us from the exam guide's task statements and the Databricks documentation — never from real exam content.
- Every option carries its own explanation and a link to the Databricks page that proves it.
- A question enters a timed exam only after an independent check; until then it is a practice question.
- Results are a plain percentage, per domain too — not an imitation of Databricks's scaled score.
Questions about the Data Engineer Associate practice exam
- Is the Data Engineer Associate practice exam free?
- Yes. The full practice exam (45 questions, 90 minutes) is free with no sign-up, and so are the 177 practice questions, the task pages and the study path. Your attempts are saved in this browser, on this device.
- How many questions does it have, and is it timed?
- 45 questions in 90 minutes: the real Data Engineer Associate pace, scaled to this form. When the time runs out, the exam is submitted as it stands.
- Are the answers explained?
- After you submit, not during: like the real exam, the timed exam gives no feedback until the end. Then you get your score overall and per exam domain, and every question with each option explained and a link to the Databricks documentation page behind it. In practice mode an answer is explained as soon as you check it.
- What does the Data Engineer Associate exam pack add?
- 3 more timed practice exams (Practice exam 1: 45 questions, 90 minutes; Practice exam 2: 45 questions, 90 minutes; Practice exam 3: 45 questions, 90 minutes), each scored and explained the same way, and the next version of this exam if it changes within your 12 months. 12 months of access. One payment, nothing renews by itself. Everything above stays free. See the pack.
- Can I get a refund?
- Yes, with no conditions: ask within 14 days of buying and you get the whole payment back. The details are in the terms.
- Is this an official Databricks practice exam?
- No. BytePatterns is not affiliated with, endorsed or sponsored by Databricks. Every question is written by us from the public exam guide and the Databricks documentation, never from real exam content, and a score here does not predict your result on the real exam. Databricks lists its own preparation resources on its certification page.