Databricks Certified Data Engineer Associate Quick Facts (2025)

Databricks Certified Data Engineer Associate Quick Facts

The Databricks Certified Data Engineer Associate certification opens the door to building strong data engineering foundations with Databricks. This overview will guide you through everything you need to know so you can step into exam preparation with clarity and confidence.

How does the Databricks Certified Data Engineer Associate certification support your data career?

The Databricks Certified Data Engineer Associate certification validates your ability to use Databricks for data engineering and pipeline development in a modern data lakehouse environment. Earning this credential shows that you can work with Delta Lake, Spark SQL, Python, and Databricks-native tools to prepare, transform, manage, and govern data at scale.

It is designed for individuals who want to demonstrate practical proficiency in building efficient pipelines, handling incremental and production-grade data operations, and applying governance standards with Unity Catalog. Whether you are early in your data career or seeking to deepen your expertise in Databricks, this certification highlights your ability to create real impact in cloud-based data solutions.

Exam Domains Covered (Click to expand breakdown)

Exam Domain Breakdown

Domain 1: Databricks Lakehouse Platform (24% of the exam)

Databricks Lakehouse Platform

Describe the relationship between the data lakehouse and the data warehouse.
Identify the improvement in data quality in the data lakehouse over the data lake.
Compare and contrast silver and gold tables, which workloads will use a bronze table as a source, which workloads will use a gold table as a source.
Identify elements of the Databricks Platform Architecture, such as what is located in the data plane versus the control plane and what resides in the customer’s cloud account.
Differentiate between all-purpose clusters and jobs clusters.
Identify how cluster software is versioned using the Databricks Runtime.
Identify how clusters can be filtered to view those that are accessible by the user.
Describe how clusters are terminated and the impact of terminating a cluster.
Identify a scenario in which restarting the cluster will be useful.
Describe how to use multiple languages within the same notebook.
Identify how to run one notebook from within another notebook.
Identify how notebooks can be shared with others.
Describe how Databricks Repos enables CI/CD workflows in Databricks.
Identify Git operations available via Databricks Repos.
Identify limitations in Databricks Notebooks version control functionality relative to Repos.

Summary: This section equips you with knowledge about how the Databricks Lakehouse Platform blends the strengths of data lakes and data warehouses. You will learn how Delta Lake improves reliability, quality, and performance compared to traditional lake implementations and how silver and gold tables support different workload needs. Understanding the architecture of the platform, including what exists in the control plane versus the data plane, will enable you to explain where data lives and how it is managed securely.

Beyond architecture, you explore how practical cluster operations affect day-to-day workflows. This means knowing the difference between cluster types, how runtimes are versioned, when to terminate or restart a cluster, and how visibility into clusters can be customized. You will also explore productivity aspects such as running multiple languages in a single notebook, executing notebooks within notebooks, or leveraging repos and CI/CD workflows to bring structure and version control into engineering projects.

Domain 2: ELT With Spark SQL and Python (29% of the exam)

ELT with Apache Spark

Extract data from a single file and from a directory of files.
Identify the prefix included after the FROM keyword as the data type.
Create a view, a temporary view, and a CTE as a reference to a file.
Identify that tables from external sources are not Delta Lake tables.
Create a table from a JDBC connection and from an external CSV file.
Identify how the count_if function and the count where x is null can be used.
Identify how the count(row) skips NULL values.
Deduplicate rows from an existing Delta Lake table.
Create a new table from an existing table while removing duplicate rows.
Deduplicate a row based on specific columns.
Validate that the primary key is unique across all rows.
Validate that a field is associated with just one unique value in another field.
Validate that a value is not present in a specific field.
Cast a column to a timestamp.
Extract calendar data from a timestamp.
Extract a specific pattern from an existing string column.
Utilize the dot syntax to extract nested data fields.
Identify the benefits of using array functions.
Parse JSON strings into structs.
Identify which result will be returned based on a join query.
Identify a scenario to use the explode function versus the flatten function.
Identify the PIVOT clause as a way to convert data from a long format to a wide format.
Define a SQL UDF.
Identify the location of a function.
Describe the security model for sharing SQL UDFs.
Use CASE/WHEN in SQL code.
Leverage CASE/WHEN for custom control flow.

Summary: This section immerses you in the core ELT workflows within Databricks, specifically Spark SQL and Python. You will practice extracting data from diverse sources, creating views, and establishing efficient data references. Beyond extraction, you explore deduplication strategies, key validation, and the importance of ensuring relational integrity across tables. These skills keep pipelines reliable and the resulting data trustworthy.

You will also gain insights into working with advanced data structures and transformations, including parsing nested JSON, using array functions, or converting data layouts with pivot operations. Control flow with CASE/WHEN, function creation, and even applying security models to shared SQL UDFs round out your understanding. By mastering these capabilities, you become proficient at shaping raw datasets into analytics-ready forms directly within Databricks.

Domain 3: Incremental Data Processing (22% of the exam)

Incremental Data Processing

Identify where Delta Lake provides ACID transactions.
Identify the benefits of ACID transactions.
Identify whether a transaction is ACID-compliant.
Compare and contrast data and metadata.
Compare and contrast managed and external tables.
Identify a scenario to use an external table.
Create a managed table.
Identify the location of a table.
Inspect the directory structure of Delta Lake files.
Identify who has written previous versions of a table.
Review a history of table transactions.
Roll back a table to a previous version.
Identify that a table can be rolled back to a previous version.
Query a specific version of a table.
Identify why Zordering is beneficial to Delta Lake tables.
Identify how vacuum commits deletes.
Identify the kind of files Optimize compacts.
Identify CTAS as a solution.
Create a generated column.
Add a table comment.
Use CREATE OR REPLACE TABLE and INSERT OVERWRITE.
Compare and contrast CREATE OR REPLACE TABLE and INSERT OVERWRITE.
Identify a scenario in which MERGE should be used.
Identify MERGE as a command to deduplicate data upon writing.
Describe the benefits of the MERGE command.
Identify why a COPY INTO statement is not duplicating data in the target table.
Identify a scenario in which COPY INTO should be used.
Use COPY INTO to insert data.
Identify the components necessary to create a new DLT pipeline.
Identify the purpose of the target and of the notebook libraries in creating a pipeline.
Compare and contrast triggered and continuous pipelines in terms of cost and latency.
Identify which source location is utilizing Auto Loader.
Identify a scenario in which Auto Loader is beneficial.
Identify why Auto Loader has inferred all data to be STRING from a JSON source.
Identify the default behavior of a constraint violation.
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
Explain change data capture and the behavior of APPLY CHANGES INTO.
Query the events log to get metrics, perform audit logging, examine lineage.
Troubleshoot DLT syntax: Identify which notebook in a DLT pipeline produced an error, identify the need for LIVE in create statement, identify the need for STREAM in from clause.

Summary: This section focuses on the mechanics of incremental data management with Delta Lake. You will learn how ACID transactions underpin reliability and how metadata management, version control, and table rollback provide resilience during pipeline evolution. Practices such as Z-ordering, vacuuming, optimizing files, and leveraging structured commands like CTAS give you control over query performance and storage.

Moving into streaming and automation, you will explore commands like MERGE, COPY INTO, and Auto Loader for handling ongoing ingestion and deduplication. Concepts such as change data capture, constraint handling, and pipeline orchestration with Delta Live Tables highlight how Databricks streamlines continuous processing. This set of skills prepares you to keep data pipelines accurate, scalable, and audit-ready.

Domain 4: Production Pipelines (16% of the exam)

Production Pipelines

Identify the benefits of using multiple tasks in Jobs.
Set up a predecessor task in Jobs.
Identify a scenario in which a predecessor task should be set up.
Review a task's execution history.
Identify CRON as a scheduling opportunity.
Debug a failed task.
Set up a retry policy in case of failure.
Create an alert in the case of a failed task.
Identify that an alert can be sent via email.

Summary: This section builds your understanding of how Databricks jobs can be composed into production-ready pipelines. You will explore the advantages of using multiple tasks, orchestrating them with dependencies, and configuring CRON schedules for regular execution. Examining task history ensures visibility into performance and supports lifecycle awareness.

You will also learn how to implement resilience. Setting up retry policies, debugging tasks, and creating timely alerts ensures pipelines remain operational without constant manual intervention. By incorporating these strategies, you elevate workflows from simple job runs into reliable, well-monitored production systems that fuel ongoing business processes with consistent data.

Domain 5: Data Governance (9% of the exam)

Data Governance

Identify one of the four areas of data governance.
Compare and contrast metastores and catalogs.
Identify Unity Catalog securables.
Define a service principal.
Identify the cluster security modes compatible with Unity Catalog.
Create a UC-enabled all-purpose cluster.
Create a DBSQL warehouse.
Identify how to query a three-layer namespace.
Implement data object access control.
Identify colocating metastores with a workspace as best practice.
Identify using service principals for connections as best practice.
Identify the segregation of business units across catalog as best practice.

Summary: This section introduces the importance of governance in Databricks environments. You will learn how metastores and catalogs are structured, and how Unity Catalog provides securables and namespace management. Creating UC-enabled clusters and DBSQL warehouses illustrates how governance ties directly into compute and query operations.

Equally valuable are the governance best practices around colocation, service principals, and secure object access. Knowing how to implement access control and align governance with organizational structures ensures scalable and compliant ecosystems. By mastering these controls, you not only safeguard data integrity but also enable your teams to operate responsibly and collaboratively within Databricks.

Who should consider the Databricks Certified Data Engineer Associate certification exam?

The Databricks Certified Data Engineer Associate certification is perfect for professionals who want to demonstrate their ability to build and manage data workflows on the Databricks Lakehouse Platform. It suits individuals such as junior data engineers, analysts transitioning into engineering roles, software developers interested in big data, and cloud-focused professionals working on Spark and Delta Lake. This certification is also an ideal stepping stone if you’re aiming to specialize in advanced data engineering or architecture roles, as it gives you credibility in managing and transforming data at scale.

By earning this credential, you’re showing employers that you not only understand the concepts behind the modern data lakehouse but that you can apply those skills to complete real engineering tasks.

What kinds of career opportunities open up with the Databricks Data Engineer Associate certification?

Holding this certification signals to employers that you can handle introductory data engineering tasks with confidence. With Databricks skills in hand, you may qualify for roles such as:

Junior Data Engineer
ETL Developer
Cloud Data Engineer
Data Operations Analyst
Spark Developer

Beyond entry roles, this certification builds the foundation for future advancement into more advanced titles such as Senior Data Engineer, Data Architect, or Machine Learning Engineer, especially when combined with additional experience and certifications.

How many questions are on the Databricks Certified Data Engineer Associate exam?

The certification exam includes 45 multiple-choice questions. Each question is carefully designed to test your applied knowledge of Spark, Delta Lake, and Databricks-specific data workflows. Some questions may feel more conceptual while others will present real-world style examples.

Since questions are multiple-choice, your focus should be on truly understanding concepts and not just memorization. The good news is that your time per question is manageable, as you’ll have an average of two minutes per question.

How long is the exam for the Databricks Data Engineer Associate certification?

You’ll be given 90 minutes to complete the entire exam. This generous time allocation means that with proper pacing, you can read through questions carefully, rule out incorrect answers, and double-check your reasoning before moving forward.

Many candidates find that the time is sufficient when they practice good exam strategy, which includes moving on from tough questions and revisiting them later. Being familiar with the exam environment will give you an additional advantage here.

What passing score is required to earn the Databricks Certified Data Engineer Associate credential?

You need a passing score of 70 out of 100. The scoring is designed to reflect overall performance, so you don’t need to hit the passing mark in each individual section. Instead, your performance across all domains is what counts.

This means that if you are stronger in certain topics like Spark SQL but less experienced in production pipelines, you can still pass by balancing your knowledge across the test. Setting a goal above the minimum passing score is always a smart approach to boost confidence.

How much does the Databricks Certified Data Engineer Associate certification exam cost?

The exam registration fee is $200 USD, plus any applicable taxes depending on your local region. It’s a smart investment into your future because Databricks is increasingly used in enterprises worldwide, and certified engineers are in high demand.

Employers often place real value on certification-backed expertise, so this cost is typically offset quickly in career growth potential and opportunities for higher-paying data engineering roles.

What languages can you take the Databricks Data Engineer Associate exam in?

The exam is available in English, Japanese (日本語), Portuguese (Português BR), and Korean (한국어). This international availability makes it accessible to data professionals globally.

Choosing to take the exam in your strongest language can help ensure you focus on applying the concepts rather than interpreting the wording, so consider this when selecting your exam language.

What type of questions should you expect on the exam?

The exam is composed entirely of multiple-choice questions. Every question is designed to assess your practical understanding of Databricks tools and Spark SQL/Python workflows.

While there are no multi-select or case study questions, don’t mistake simplicity of format for ease. The questions test real understanding of concepts such as incremental pipelines, governance with Unity Catalog, and multi-hop transformations.

What domains are covered in the Databricks Certified Data Engineer Associate exam, and how are they weighted?

The exam blueprint consists of five domains with specific weightings:

Databricks Lakehouse Platform (24%) – Focuses on the architecture, clusters, Repos, and notebooks.
ELT with Spark SQL and Python (29%) – Covers extraction, transformation, deduplication, UDFs, joins, and SQL operations.
Incremental Data Processing (22%) – Involves Delta Lake, ACID transactions, versioning, MERGE operations, COPY INTO, Auto Loader, and DLT.
Production Pipelines (16%) – Centers on building and managing tasks, job scheduling, error handling, and alerts.
Data Governance (9%) – Focuses on Unity Catalog, access controls, service principals, security modes, and best practices for multi-unit data structures.

By balancing your study with these percentages, you can optimize your training to align with where most points are available.

What prerequisites or background knowledge are recommended?

There are no mandatory prerequisites, meaning anyone can register, but it’s recommended that candidates have at least six months of hands-on experience performing data engineering tasks on Databricks.

Additionally, familiarity with SQL and Python is extremely helpful, as the certification test code examples are provided in these two languages. Having a background in cloud services and data workflows will also make the experience smoother.

How long does your certification remain valid after passing?

Once earned, the Databricks Certified Data Engineer Associate credential is valid for two years. To keep your certification current, you’ll need to pass the updated exam that is active at your time of renewal.

Renewing on schedule ensures your skills stay aligned with the latest Databricks advancements, which employers find valuable as data engineering evolves rapidly.

Are there unscored items on the exam?

Yes, like many certification exams, Databricks occasionally includes unscored content on the exam. These experimental questions do not impact your score and are included to test possible future additions to the exam pool.

The great part is that extra time is already built into your exam duration, so you don’t have to worry about losing time due to these items.

How do I register for the Databricks Certified Data Engineer Associate certification?

Exam registration is done through the official certification platform. You will first log in or create an account on Databricks’ test delivery system to schedule your exam.

Once registered, you’ll select your preferred time zone, exam time, and whether you’d like to sit the exam from home with online proctoring, which is the standard delivery method.

Can you take the Databricks Data Engineer Associate exam online?

Yes, the exam is administered through an online proctored environment. This means you can take it from the comfort of your home or office, provided you have a working webcam, reliable internet connection, and a quiet space.

Being able to test remotely adds great convenience, especially for professionals balancing coursework, projects, or full-time jobs.

What is the format for incremental data processing questions?

This domain makes up 22% of the exam and includes a focus on Delta Lake features. Expect to see questions where you need to identify behaviors of ACID transactions, rollback processes, ZORDER optimization, and scenarios where Auto Loader, DLT, or MERGE statements are best applied.

Understanding real-world data ingestion patterns and incremental updates will give you an edge in tackling this type of question with confidence.

How difficult is the Databricks Certified Data Engineer Associate exam?

The Databricks exam is widely seen as an introductory-level data engineering certification that focuses on building foundational knowledge. While it does test a range of concepts, candidates who prepare using hands-on practice in Databricks and review the exam guide thoroughly find the exam very achievable.

To further boost your readiness, you can use top-rated Databricks Certified Data Engineer Associate practice exams that mirror the real test experience with detailed explanations.

How do Databricks production pipeline topics appear on the exam?

The Production Pipelines section carries 16% of the exam weighting, focusing on your ability to design and manage jobs in Databricks. This format tests knowledge of scheduling jobs with CRON, setting dependencies, identifying failed tasks, and defining retry alerts.

If you’ve set up jobs in Databricks before, you’ll recognize many of these concepts. It is about showing you can operationalize data processes for production.

What are the main areas of Data Governance to study?

Data Governance accounts for 9% of the exam. It focuses on Unity Catalog securables, proper workspace alignment with metastores, and best practices such as segregating business units and using service principals.

While smaller in percentage, governance plays an essential role in enterprise data engineering, so don’t overlook it as it could be the key differentiator in hitting the passing score.

How can I best prepare for the Databricks Certified Data Engineer Associate exam?

A great preparation strategy includes:

Taking Databricks Academy courses like Data Ingestion with Delta Lake and Build Data Pipelines with Delta Live Tables.
Gaining hands-on practice directly in the Databricks environment.
Reviewing the detailed exam guide to close knowledge gaps.
Testing your knowledge with practice exams for endurance and accuracy.

The combination of training, real-world projects, and structured practice is the surest way to walk into the exam with confidence.

How important is SQL and Python knowledge for this exam?

Very important: exam questions use SQL wherever possible, and Python in cases where SQL isn’t suitable. Knowing both languages at a functional level will help you quickly understand questions and identify the correct solution.

If you’re stronger in SQL or Python individually, consider brushing up on your weaker area in advance. Exam scenarios sometimes mix both into realistic workflows.

Where can I find the official exam information?

You can always confirm requirements and updates by visiting the official Databricks Certified Data Engineer Associate certification page. Checking the official site ensures you’re aligned with the latest details and any future updates.

The Databricks Certified Data Engineer Associate certification is your opportunity to stand out in the growing field of data engineering. With the right preparation, training resources, and hands-on practice, you’ll be equipped not only to pass the exam but to succeed in projects that use one of the most innovative data platforms in the industry. This certification is a powerful addition to your resume and a clear signal that you’re ready to thrive in the world of big data and analytics.

Databricks Certified Data Engineer Associate Quick Facts (2025)

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