DATABRICKS

Databricks Certified Data Engineer Associate

DATA-ENGINEER-ASSOCIATE

The Databricks Certified Data Engineer Associate certification validates your ability to use the Databricks Data Intelligence Platform for foundational data engineering tasks. It covers data ingestion, ETL with PySpark, Lakeflow Jobs, CI/CD, and governance. Ideal for data engineers building and maintaining batch and streaming pipelines, this credential demonstrates you can turn raw data into reliable, governed analytics-ready datasets.

Practice Free Questions

7Domains

33Objectives

137Practice pages

623Free questions

DATA-ENGINEER-ASSOCIATE free practice questions

Choose any objective and open any question page — no sign-in required.

Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Understand 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.

15 questions · 3 pages

Questions 1–5 6–10 11–15

Enable 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.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity‑Catalog–governed tables.

17 questions · 4 pages

Questions 1–5 6–10 11–15 16–17

Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into UnityCatalog–governed tables.

33 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–33

Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into UnityCatalog–governed tables.

36 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–36

Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into UnityCatalog–governed tables, usually orchestrated and scheduled with Lakeflow Jobs.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Prioritize 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.

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into UnityCatalog–governed Delta tables.

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.

11 questions · 3 pages

Questions 1–5 6–10 11–11

Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.

13 questions · 3 pages

Questions 1–5 6–10 11–13

Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.

15 questions · 3 pages

Questions 1–5 6–10 11–15

Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.

10 questions · 2 pages

Questions 1–5 6–10

Understand the basic tuning parameters (spark.sql.shuffle.partitions:, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Understand 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.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.

14 questions · 3 pages

Questions 1–5 6–10 11–14

Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration

12 questions · 3 pages

Questions 1–5 6–10 11–12

Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG‑based task graph

15 questions · 3 pages

Questions 1–5 6–10 11–15

Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update)

11 questions · 3 pages

Questions 1–5 6–10 11–11

Choose between time‑based and data‑driven triggers based on data availability and pipeline dependencies.

11 questions · 3 pages

Questions 1–5 6–10 11–11

Manage 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.

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Understand environment-specific configuration using Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Deploy 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.

17 questions · 4 pages

Questions 1–5 6–10 11–15 16–17

Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows.

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Use 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.

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.

22 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–22

Understand the features of Liquid Clustering and predictive optimization.

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Diagnose cluster startup failures, library conflicts, and out-of-memory issues.

7 questions · 2 pages

Questions 1–5 6–7

Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them.

8 questions · 2 pages

Questions 1–5 6–8

Configure 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.

22 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–22

Understand column-level masking and row-level security to restrict data visibility based on user groups.

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.

15 questions · 3 pages

Questions 1–5 6–10 11–15

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (33 objectives · 137 pages).

Databricks Certified Data Engineer Associate

DATA-ENGINEER-ASSOCIATE free practice questions

Domain 1 · Databricks Intelligence Platform6% of practice bank · 2 objectives · 39 questions6%

Domain 2 · Data Ingestion and Loading31% of practice bank · 7 objectives · 191 questions31%

Domain 3 · Data Transformation and Modeling16% of practice bank · 7 objectives · 102 questions16%

Domain 4 · Working with Lakeflow Jobs8% of practice bank · 4 objectives · 49 questions8%

Domain 5 · Implementing CI/CD14% of practice bank · 4 objectives · 89 questions14%

Domain 6 · Troubleshooting, Monitoring, and Optimization14% of practice bank · 5 objectives · 88 questions14%

Domain 7 · Governance and Security10% of practice bank · 4 objectives · 65 questions10%