Databricks Certified Data Engineer Associate Certification Guide - Syllabus, Exam Details and Sample

Databricks • Data Engineer Associate

CERTIFICATION GUIDE

45 Questions. Seven Domains. One Lakehouse.

The Databricks Certified Data Engineer Associate certification, mapped Every one of the seven exam domains with its official weighting, and the full objective list as a study checklist — plus the exam fact sheet, what the May 2026 rewrite changed, what the role pays in 2026, a five-step route from first read to booked seat, and ten real sample questions with a complete answer key.

45

90

70%

$200

2 yrs

QUESTIONS

MINUTES

PASSING SCORE

EXAM FEE

VALIDITY

Prepared by CertFun • www.certfun.com

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Databricks Data Engineer Associate

SECTION 01

Exam Overview The Databricks Certified Data Engineer Associate certification is the entry point to the Databricks data engineering track — and, since the May 2026 rewrite, a noticeably more modern exam than the one most study guides describe. It is 45 scored questions in 90 minutes, and it wants evidence you have actually built pipelines on the platform, not just read about them. Everything below comes from Databricks' own certification page and CertFun's Data Engineer Associate exam syllabus.

Certification

Databricks Certified Data Engineer Associate

Exam version

Version 3 — current blueprint live since 4 May 2026

Number of questions

45 scored questions

Exam duration

90 minutes

Passing score

70%

Exam fee

USD 200 registration fee

Delivery

Online proctored or test centre

Validity period

2 years — recertify on the then-current exam version

Prerequisites

None — hands-on data engineering experience recommended

Languages

English, Japanese, Portuguese (Brazil), Korean

Scheduling

Databricks Webassessor

Two things catch people out. The credential lasts two years, and renewal means sitting whatever the current version is — not the one you originally passed. And there are no formal prerequisites, which tempts people to book early; the exam guide instead assumes hands-on time with the tasks it lists.

Registration goes through Databricks Webassessor.

SECTION 02

What the 2026 Rewrite Changed If you are revising from older material, this is the section to read twice. The current blueprint has moved decisively toward how Databricks ships work today, and several topics that dominate the new objective list simply do not appear in pre-2026 study guides.

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New or much heavier in the current blueprint Lakeflow Connect and Lakeflow Jobs — named directly, and worth 16% on their own.

Declarative Automation Bundles deployed across dev, test and prod, driven by the CLI. Unity Catalog ABAC policies for row-level filtering and column masking.

Liquid Clustering and predictive optimization under troubleshooting.

A full CI/CD domain — Git Folders, branches, pull requests — worth 10%.

The through-line is that the exam now treats a data engineer as someone who ships and operates pipelines, not just writes transformations. Governance and CI/CD together are worth a quarter of the paper. The wider Databricks catalogue sits on the CertFun Databricks vendor page.

SECTION 03

The Seven Domains, by Weight Unlike many vendors, Databricks publishes exact weightings — so you can aim your study time precisely rather than guessing. Transformation and ingestion alone are 43% of the exam; add governance and you are at 58% across three domains. The platform-overview domain, the one most beginners over-revise, is worth just 6%.

Databricks Intelligence Platform

6% 21%

Data Ingestion and Loading

22%

Data Transformation and Modeling

16%

Working with Lakeflow Jobs Implementing CI/CD

10%

Troubleshooting, Monitoring, and Optim…

10%

Governance and Security

15%

Official domain weightings from the Databricks exam guide.

The full objective list follows, straight from the exam syllabus. Treat it as a checklist — if you cannot demonstrate a line in a real workspace, that is a study item.

01. Databricks Intelligence Platform — 6% • Understand core components including architecture, Delta Lake, and Unity Catalog • Understand compute services characteristics, limitations, cost models • Select suitable compute options for workload use cases

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02. Data Ingestion and Loading — 21% • Enable batch, streaming, and incremental loading patterns • Import data from local files, Lakeflow Connect standard and managed connectors • Use COPY INTO for incremental cloud storage loads (ADLS/S3/GCS) into Unity Catalog tables • Configure Auto Loader with schema enforcement and evolution for batch modes • Set up Lakeflow Connect for enterprise source ingestion • Use JDBC/ODBC or REST clients in notebooks for data landing • Prioritize ingestion methods based on volume, frequency, data types, governance needs • Ingest semi-structured and unstructured data via connectors into Delta tables

03. Data Transformation and Modeling — 22% • Implement cleaning: read bronze tables, handle nulls, standardize types, write to silver tables • Combine DataFrames: inner join, left join, broadcast join, multiple keys, cross join, union • Manipulate columns, rows, structures: add, drop, split, rename, filter, explode arrays • Perform deduplication and aggregation operations (count, approximate count distinct, mean,

summary) • Understand tuning parameters: spark.sql.shuffle.partitions, spark.default.parallelism, memory

settings, autoBroadcastJoinThreshold • Build Gold layer objects: materialized views, views, streaming tables for BI/analytics • Apply data quality checks and validation rules

04. Working with Lakeflow Jobs — 16% • Implement control flows: retries, conditional tasks, branching, looping • Configure common tasks: notebook, SQL query, dashboard, pipeline tasks with dependencies • Implement job schedules with trigger types: scheduled, file arrival, table update • Choose between time-based and data-driven triggers

05. Implementing CI/CD — 10% • Manage code workflow in the Databricks workspace UI • Create and switch branches in Databricks Git Folders • Commit, push changes, create pull requests via Git integration • Understand environment-specific configuration using Automation Bundle variables and overrides • Deploy Declarative Automation Bundles across dev, test, prod environments • Use the Databricks CLI for validation, deployment, and management

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06. Troubleshooting, Monitoring, and Optimization — 10% • Identify job performance trends using run history and execution time comparisons • Monitor pipeline health: interpret job statuses, view DAG task graphs, track run times and failure

rates • Identify bottlenecks: data skew, shuffling, disk spilling via Spark UI metrics • Understand Liquid Clustering and predictive optimization features • Diagnose cluster startup failures, library conflicts, out-of-memory issues

07. Governance and Security — 15% • Differentiate managed vs. external tables in Unity Catalog • Perform operations: create, modify, delete, convert between table types • Configure access controls via UI and SQL using GRANT, REVOKE, DENY privileges • Apply privileges to principals (users, groups, service principals) at security hierarchy levels • Understand column-level masking and row-level security • Understand Unity Catalog ABAC policies for row-level filtering and column masking

SECTION 04

What the Credential Is Worth Data engineering pay has held up well, and Databricks skills sit near the top of it. In the US market, roles tied to this certification average around $82,600 a year, with most postings between $65,500 and $95,000 depending on experience and location. The certification is not a salary lever on its own — it is a filter. Analyses of data engineering job postings put Databricks credentials among the ones that actually recur in requirements, alongside the cloud-provider data certifications, which matters most when your CV is being screened before anyone reads it.

Why this one, specifically It is the entry credential for the whole Databricks track — everything else builds on it.

The blueprint is current: Lakeflow, Unity Catalog governance, CI/CD, not legacy tooling.

No prerequisites, so it is reachable without a prior Databricks certification.

Two-year validity keeps holders on a platform that changes fast.

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SECTION 05

Getting Certified, Step by Step 1

Read the current exam guide, not an old one Confirm you are on the version live since 4 May 2026 before you revise anything.

2

Work the Data Engineering learning path Databricks Academy courses on Lakeflow ingestion, jobs and pipeline building.

3

Build in a real workspace Auto Loader, a bronze-silver-gold flow, a Lakeflow job, and Unity Catalog grants.

4

Rehearse under exam conditions Forty-five questions in 90 minutes — two minutes each. Practise scenarios, not recall.

5

Book through Webassessor Online proctored or a test centre. Passing bar is 70%; the credential lasts two years.

For step two, the Data Engineering learning catalogue is the vendor's own catalogue. For step four, CertFun's Data Engineer Associate practice exam mirrors the real format so you find the gaps before the exam does.

SECTION 06

Databricks Data Engineer Associate Sample Questions These ten questions come from CertFun's Data Engineer Associate sample questions page, shown here in shuffled order. Note the shape of them: several are code-reading questions where three options are plausible and one detail decides it — a count vs. a count_distinct, an INSERT INTO vs. an INSERT. That is exactly how the real paper reads. Answers are at the end of the section.

Q1. Two junior data engineers are authoring separate parts of a single data pipeline notebook.

They are working on separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer experienced in Databricks suggests there is a better alternative for this type of collaboration. Which of the following supports the senior data engineer's claim? a) Databricks Notebooks support automatic change-tracking and versioning b) Databricks Notebooks support real-time coauthoring on a single notebook c) Databricks Notebooks support commenting and notification comments d) Databricks Notebooks support the use of multiple languages in the same notebook e) Databricks Notebooks support the creation of interactive data visualizations

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Q2. Which of the following describes a scenario in which a data engineer will want to use a Job cluster instead of an all-purpose cluster? a) An ad-hoc analytics report needs to be developed while minimizing compute costs. b) A data team needs to collaborate on the development of a machine learning model. c) An automated workflow needs to be run every 30 minutes. d) A Databricks SQL query needs to be scheduled for upward reporting. e) A data engineer needs to manually investigate a production error.

Q3. A data engineer is configuring Delta Sharing for a multi-team project where teams from different departments will need to access shared data. The data engineer has successfully created a Unity Catalog metastore and is now setting up the Delta Share. The goal is to ensure that internal teams can access the data with full permissions, while external partners can only read the shared data. Which action should the data engineer take? a) Grant READ permissions to external partners through the Delta Share and READ/WRITE permissions to internal teams on Unity Catalog. b) Create a Delta Share, add the internal team's tables and views, and assign READ/WRITE permissions to both external partners and internal teams. c) Create READ permissions to external partners and READ/WRITE permissions to internal teams through Delta Share. d) Create a Delta Share, set up a secure access URL for internal teams and external partners, and distribute the URL to provide them access to the shared data.

Q4. A data engineer is curating data in the silver layer of a hospital management data warehouse system. The data engineer is trying to aggregate hospital billing data from a table patient_billing to generate a daily revenue fact table daily_revenue. Which code snippet aggregates the amount billed per day with the unique invoices from a DataFrame billing_df? a) billing_df.groupBy("billing_date").agg( sum("amount_billed").alias("total_revenue"), sum("billing_id").alias("total_invoices") ) b) billing_df.groupBy("billing_date").agg( col("amount_billed").alias("total_revenue"), count("billing_id").alias("total_invoices") ) c) billing_df.groupBy("billing_date").agg( sum("amount_billed").alias("total_revenue"), count_distinct("patient_id").alias("total_invoices") ) d) billing_df.groupBy("billing_date").agg( sum("amount_billed").alias("total_revenue"), count_distinct("billing_id").alias("total_invoices") )

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Q5. A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Development mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline? a) All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing. b) All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing. c) All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated. d) All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

Q6. A new data engineering team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project. Which command can be used to grant full permissions on the database to the new data engineering team? a) GRANT SELECT ON TABLE sales TO team; b) GRANT USAGE ON TABLE sales TO team; c) GRANT ALL PRIVILEGES ON TABLE sales TO team; d) GRANT ALL PRIVILEGES ON TABLE team TO sales; Q7. A data engineer is working on a Databricks project with a schema named sales_data that stores transactional sales information. The analyst group, responsible for analyzing this data, needs read-only access to the sales_data schema. Which SQL command should the data engineer use, assuming that the analyst group already has USE CATALOG and USE SCHEMA permissions? a) GRANT ALL PRIVILEGES ON SCHEMA sales_data TO analysts; b) GRANT SELECT ON SCHEMA sales_data TO analysts; c) GRANT INSERT ON SCHEMA sales_data TO analysts; d) GRANT SELECT ON ALL TABLES IN SCHEMA sales_data TO analysts;

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Q8. A data engineer has determined that a table of a given format is necessary. Which code block is used by a SQL DDL command to create an empty Delta table in that format, regardless of whether a table already exists with this name? a) CREATE OR REPLACE TABLE table_name WITH COLUMNS ( employeeId STRING, startDate DATE, avgRating FLOAT ) USING DELTA b) CREATE TABLE IF NOT EXISTS table_name ( employeeId STRING, startDate DATE, avgRating FLOAT ) c) CREATE TABLE table_name AS SELECT employeeId STRING, startDate DATE, avgRating FLOAT d) CREATE OR REPLACE TABLE table_name ( employeeId STRING, startDate DATE, avgRating FLOAT ) Q9. A data engineer has been given a new record of data: id STRING = 'a1', rank INTEGER = 6, rating FLOAT = 9.4. Which SQL command can be used to append the new record to an existing Delta table my_table? a) UPDATE VALUES ('a1', 6, 9.4) my_table b) UPDATE my_table VALUES ('a1', 6, 9.4) c) INSERT VALUES ('a1', 6, 9.4) INTO my_table d) INSERT INTO my_table VALUES ('a1', 6, 9.4) Q10. A data architect is designing a data model that works for both video-based machine learning workloads and highly audited batch ETL/ELT workloads. Which consideration supports a data lakehouse for this design? a) A data lakehouse requires very little data modeling. b) A data lakehouse combines compute and storage for simple governance. c) A data lakehouse provides autoscaling for compute clusters. d) A data lakehouse fully exists in the cloud. e) A data lakehouse stores unstructured data and is ACID-compliant.

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q10

b

c

a

d

d

c

b

d

d

e

Answer key

SECTION 07

Where to Go Next Because the blueprint changed recently, first-hand accounts are more useful than usual right now — the Databricks Community forum has active threads on what the current guide expects and which Academy courses map to it.

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Quick reference Official certification page — databricks.com Exam syllabus + practice test — certfun.com/databricks Exam-guide discussion threads — community.databricks.com Scheduling — webassessor.com/databricks

Before you book — the guide in one card Questions

45

Duration

90 minutes

Passing score

70%

Fee

USD 200

Validity

2 years

Prerequisites

None

Live since 4 May 2026

Delivery

Online or test centre

Next heaviest

Ingestion & Loading (21%)

Domains

7

Current version Heaviest

Transformation & Modeling

domain

(22%)

Lightest domain

Intelligence Platform (6%)

Good luck — and keep the weightings in front of you while you revise. Two domains carry nearly half this exam, and most people spend their first week on the one worth 6%.

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