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Databricks · Associate

Databricks Data Engineer Associate

Ingest, transform, orchestrate and govern data on the Databricks Data Intelligence Platform. Lessons, exam-style practice, timed mocks and an AI tutor, mapped to every objective of the official exam guide.

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41lessons
395exam-style questions
4practice exams
12hands-on labs

The exam

Questions
45
Time
90 minutes
Passing score
Not published by Databricks; this kit targets ≥ 80% on unseen mocks
Exam fee
USD 200 (plus applicable taxes)
Valid for
2 years (renew by taking the current exam or the 25-question renewal exam)
Delivery
Online proctored or test center

Official exam page · booked with Databricks

Syllabus

Every section of the exam guide

7 sections and 33 objectives, in the official weights. Each lesson is mapped to the objectives it covers.

Section 1 · Databricks Intelligence Platform6%

What the exam asks

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

Lessons

  1. Data Intelligence Platform Architecture
  2. Unity Catalog Overview
  3. Compute Options & Cost Models
  4. Workspace & Notebook Essentials
Section 2 · Data Ingestion and Loading21%

What the exam asks

  • 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.
  • Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity-Catalog–governed tables.
  • Use 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.
  • Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into Unity Catalog–governed tables.
  • Use 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.
  • 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.
  • Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into Unity Catalog–governed Delta tables.

Lessons

  1. Ingestion Patterns & Local File Uploads
  2. COPY INTO
  3. Auto Loader
  4. Lakeflow Connect
  5. JDBC/ODBC & REST Ingestion from Notebooks
  6. Semi-Structured & Unstructured Data
  7. Choosing the Right Ingestion Method
Section 3 · Data Transformation and Modeling22%

What the exam asks

  • Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.
  • Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.
  • Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.
  • Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.
  • Understand the basic tuning parameters (spark.sql.shuffle.partitions, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.
  • 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.
  • Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.

Lessons

  1. Medallion Architecture & Bronze→Silver Cleaning
  2. Joins & Set Operations
  3. Column, Row & Structure Manipulation
  4. Deduplication & Aggregations
  5. Spark Tuning Parameters & Measuring Performance
  6. Gold Layer Objects
  7. Lakeflow Spark Declarative Pipelines
  8. Data Quality & Validation
Section 4 · Working with Lakeflow Jobs16%

What the exam asks

  • Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration.
  • Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG-based task graph.
  • Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update).
  • Choose between time-based and data-driven triggers based on data availability and pipeline dependencies.

Lessons

  1. Lakeflow Jobs Fundamentals
  2. Task Dependencies & Control Flow
  3. Triggers & Schedules
  4. Time-based vs Data-driven Orchestration
Section 5 · Implementing CI/CD10%

What the exam asks

  • 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.
  • 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.
  • 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.
  • 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.

Lessons

  1. Databricks Git Folders
  2. Declarative Automation Bundles: Structure & Configuration
  3. Databricks CLI & CI/CD Workflows
Section 6 · Troubleshooting, Monitoring, and Optimization10%

What the exam asks

  • Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.
  • 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.
  • Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.
  • Understand the features of Liquid Clustering and predictive optimization.
  • Diagnose cluster startup failures, library conflicts, and out-of-memory issues.

Lessons

  1. Monitoring Lakeflow Jobs
  2. Spark UI & Performance Bottlenecks
  3. Liquid Clustering & Predictive Optimization
  4. Diagnosing Failures
Section 7 · Governance and Security15%

What the exam asks

  • 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.
  • 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.
  • Understand column-level masking and row-level security to restrict data visibility based on user groups.
  • Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.

Lessons

  1. Managed vs External Tables
  2. Privileges & Access Control
  3. Row Filters & Column Masks
  4. ABAC Policies & Governed Tags
  5. Lineage, Auditing & Secure Sharing
Study plan

30-Day Standard Plan

The default path for engineers with some Spark or SQL experience and up to about six months on Databricks. It covers all 41 lessons and 12 labs in order, with a domain drill after each module and daily flashcards. Mock Exam 1 on day 20 is a diagnostic, Mock Exams 2–3 measure progress, and Mock Exam 4 plus weak-area review closes it out. Day 30 is a light day, so you sit the exam on day 31. The heaviest sections (d3 22%, d2 21%, d4 16%, d7 15%) get the most days. About 1.5–2 hours a day.

  1. Day 1Kick-off: exam map, lakehouse and Spark basics
  2. Day 2DataFrames and Spark SQL
  3. Day 3Delta Lake and Structured Streaming
  4. Day 4Platform architecture and Unity Catalog
  5. Day 5Compute, cost models and the workspace
  6. Day 6Ingestion patterns and COPY INTO
  7. Day 7Auto Loader deep dive
  8. … and 23 more days, with mock exams at the end

Read the free study guide for this exam

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Sample questions

Written for practice in the style of the real exam: never questions from the real exam.

Sample question 1

A team runs a nightly batch ETL job on serverless compute. The job starts at 1:00 AM, usually finishes in 40 minutes, and its output must be ready by 7:00 AM. It uses only DataFrame APIs and supported configurations. Management asks to reduce the cost of this job. What should the data engineer change?

  1. AMove the job to an all-purpose cluster with auto termination set to 10 minutes
  2. BTurn off the Performance optimized setting so the job runs in standard performance mode
  3. CCreate an instance pool with warm idle instances and attach it to the serverless job
  4. DSet spark.sql.shuffle.partitions to 2000 so the job finishes faster and consumes fewer DBUs
Show the answer

B. Serverless jobs can run in performance-optimized or standard mode. Standard mode uses less compute and therefore fewer DBUs, at the price of a typical 4-6 minute startup. A job with a six-hour window easily absorbs that latency. In the job JSON the setting is performance_target: STANDARD, and in the UI you turn off *Performance optimized*.

Sample question 2

A 30 TB table was liquid-clustered by customer_id. Analysts now filter mostly on device_id and event_date, so the engineer runs ALTER TABLE iot.events CLUSTER BY (device_id, event_date). The next day, queries on historical data filtered by device_id are no faster. What should the engineer do?

  1. ARun OPTIMIZE iot.events FULL to recluster all existing data with the new keys
  2. BRun OPTIMIZE iot.events ZORDER BY (device_id) to co-locate rows by the new filter column
  3. CRecreate the table with PARTITIONED BY (device_id)
  4. DRun VACUUM iot.events to remove the files clustered by the old key
Show the answer

A. Changing clustering keys only updates metadata: new writes and later incremental OPTIMIZE runs use the new keys, but existing files keep their old layout. OPTIMIZE ... FULL reclusters all records with the current keys (it can take hours on very large tables), after which data skipping on device_id works across history.

Questions

About this exam

What is the passing score?

Databricks doesn't publish it and says it can change. Community reports usually put it around 70%. This kit's readiness target is ≥ 80% on two mocks you haven't seen before, with ≥ 70% in every domain.

How many questions come from each section?

Databricks publishes only percentage weights. On 45 scored items those are roughly 3 (Platform, 6%), 9 (Ingestion, 21%), 10 (Transformation, 22%), 7 (Lakeflow Jobs, 16%), 4–5 each for CI/CD and Troubleshooting (10% each) and 7 (Governance, 15%). Individual exam forms vary slightly.

Are there unscored questions?

Possibly. The exam guide says exams may include unscored items used to gather statistics. They aren't identified and don't affect your score, and extra time is built in for them. Treat every question as scored.

How much does the exam cost, and what's the retake policy?

USD 200 plus applicable taxes. Per the Databricks certification FAQ, you can register again and retake after 14 days. Each attempt costs the full fee, and Databricks doesn't issue free retake vouchers. Check the current policy on the exam page before you book.

How long is the certification valid, and how do I recertify?

Two years from the date it's awarded. To renew, pass the current live exam again, or take the renewal exam: 25 questions in 60 minutes, online proctored only, for anyone whose Data Engineer Associate credential has expired or expires within 2 months. It covers every section except the Databricks Intelligence Platform, with more weight on ingestion (32%) and transformation (24%). See the Renewal page for your dates, the outline and practice renewal exams.

Can I take it online?

Yes. It's offered online with a proctor or at a test center. You register and schedule through Webassessor, and Kryterion delivers the exam. Online exams need Kryterion's secure browser, a webcam, a valid photo ID and a clear, private room.

What languages is the exam offered in?

The exam page lists English, Japanese, Portuguese (Brazil) and Korean. Language availability can change, so confirm on the exam page when you register.

Is the code on the exam Python or SQL?

Both. The exam guide refers to PySpark and SQL for cleaning, joins, aggregation and pipelines, and you'll also see bundle YAML and Databricks CLI commands. This kit uses Python (PySpark and pyspark.pipelines) and SQL throughout. You don't need Scala.

Which Databricks Runtime version does the exam assume?

The guide doesn't name a runtime. It reflects the current platform: Unity Catalog everywhere, serverless compute, Lakeflow Connect, Jobs and Declarative Pipelines, liquid clustering, ABAC. Learn behaviors and defaults, not runtime version numbers.

Will old names like Delta Live Tables, Workflows or Repos appear?

The exam guide uses current names (Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, Git folders, Declarative Automation Bundles) and mentions some former names in parentheses. Learn both. Judge every option on what it does, not on which product name it uses.

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