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Databricks Certified Data Engineer Professional Quick Facts
The Databricks Certified Data Engineer Professional certification empowers you to elevate your skills and confidently demonstrate mastery of advanced data engineering concepts. This overview gives you clarity on the exam structure, helping you focus on the most important areas of preparation with confidence and positivity.
Why pursue the Databricks Certified Data Engineer Professional certification?
This certification validates advanced expertise in building, deploying, monitoring, and optimizing data systems using Databricks. It is designed for experienced data engineers who use Databricks to work with batch and streaming data, optimize Delta Lake performance, enforce governance, and monitor pipelines in production. Successful candidates highlight their ability to make impactful architectural decisions, optimize performance for real-world workloads, and apply best practices that bring scalability and reliability to modern data platforms.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: Databricks Tooling (20% of the exam)
Databricks Tooling
- Explain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durability
- Describe how Delta Lake’s Optimistic Concurrency Control provides isolation, and which transactions might conflict
- Describe basic functionality of Delta clone
- Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes
- Implement Delta tables optimized for Databricks SQL service
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)
Databricks Tooling summary: In this section, you will focus on how Delta Lake strengthens reliability and efficiency through thoughtful design. By mastering how the transaction log and cloud storage enforce properties like atomicity and durability, along with concurrency controls for isolation, you gain the confidence to manage conflicts and guarantee consistent results even in heavily accessed environments. You’ll also learn how Delta clone supports experimentation and operational workflows without impacting source tables.
Beyond reliability, this section also emphasizes how optimizations like partitioning, z-ordering, bloom filters, and file management dramatically improve query and storage efficiency. By exploring implementation strategies, you will see how to align partitioning decisions with the data and workload patterns that produce the best results. This knowledge directly supports great outcomes when scaling SQL workloads on Databricks by matching techniques to real-world requirements.
Domain 2: Data Processing (30% of the exam)
Data Processing (Batch processing, Incremental processing, and Optimization)
- Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)
- Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files
- Articulate multiple strategies for updating 1+ records in a spark table (Type 1)
- Implement common design patterns unlocked by Structured Streaming and Delta Lake
- Explore and tune state information using stream-static joins and Delta Lake
- Implement stream-static joins
- Implement necessary logic for deduplication using Spark Structured Streaming
- Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read
- Leverage CDF to easily propagate deletes
- Demonstrate how proper partitioning of data allows for simple archiving or deletion of data
- Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries
Data Processing summary: This section equips you to work fluently across both batch and streaming contexts with Databricks, mastering techniques for partitioning and file management that optimize throughput. You’ll learn when to use hints like coalesce or repartition and understand trade-offs to control parallelism. You’ll also gain skills in writing PySpark DataFrames to disk while controlling part-file sizes, an often overlooked detail that has significant impact on downstream performance. Additionally, strategies for applying Type 1 updates to Spark tables are explored, highlighting approaches for different operational scenarios.
Equally important, knowledge expands into streaming workloads and advanced Delta Lake use cases. This includes implementing deduplication, mastering stream-static joins, and applying Change Data Feed (CDF) to design pipelines that simplify updates and deletions. Recognizing how issues like tiny files or poor partitioning can create unnecessary overhead will help you proactively design systems that remain efficient as data volumes grow. The emphasis is on building confidence in designing consistent patterns that scale smoothly.
Domain 3: Data Modeling (20% of the exam)
Data Modeling
- Describe the objective of data transformations during promotion from bronze to silver
- Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture
- Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables
- Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads
- Implement best practices when streaming data from multiplex bronze tables
- Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver
- Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake
- Implement tables avoiding issues caused by lack of foreign key constraints
- Add constraints to Delta Lake tables to prevent bad data from being written
- Implement lookup tables and describe the trade-offs for normalized data models
- Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads
- Implement SCD Type 0, 1, and 2 tables
Data Modeling summary: This section highlights how to structure and evolve your data in ways that reinforce both quality and efficiency. Transformations from bronze to silver layers ensure that data is cleansed, deduplicated, and optimized for use, while embracing Delta Lake features like CDF makes it straightforward to propagate updates and deletes. The coverage of clones introduces strategies for duplicating entire tables while balancing speed, independence, and linkage to source tables.
Further, this domain spotlights common patterns and pitfalls in lakehouse design. You’ll explore how streaming workloads can be effectively productionalized with multiplex bronze tables while avoiding unnecessary complexity, alongside best practices for enforcing constraints to prevent invalid data. Finally, advanced modeling concepts like lookup table trade-offs and designing Slowly Changing Dimensions (Types 0, 1, and 2) guide you in handling long-term evolution of business data, bringing together real-time processing needs with traditional warehouse structures.
Domain 4: Security and Governance (10% of the exam)
Security & Governance
- Create Dynamic views to perform data masking
- Use dynamic views to control access to rows and columns
Security & Governance summary: In this section, you’ll explore how to use dynamic views as a powerful tool for enforcing governance and security policies. By masking sensitive information and controlling granular access at the row or column level, you can align governance practices directly with organizational requirements. These implementations provide strong safeguards without complicating the user experience for authorized users.
The focus is on designing governance approaches that not only protect data but also improve usability and trust. As you pair governance with Databricks capabilities, you empower teams to collaborate securely while meeting compliance demands. This domain builds confidence that your workloads uphold high standards of privacy, access control, and alignment with regulation.
Domain 5: Monitoring and Logging (10% of the exam)
Monitoring & Logging
- Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications
- Inspect event timelines and metrics for stages and jobs performed on a cluster
- Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications
- Design systems that control for cost and latency SLAs for production streaming jobs
- Deploy and monitor streaming and batch jobs
Monitoring & Logging summary: This section emphasizes leveraging monitoring tools to ensure performance, reliability, and efficiency across streaming and batch jobs. By reading and interpreting information from Spark UI, Ganglia UI, and Cluster UI, you develop the ability to identify bottlenecks and debug issues with clarity. Understanding event timelines and metrics further strengthens your ability to optimize Spark applications with confidence.
These monitoring practices extend into ensuring operational alignment with cost and latency service-level agreements. With the right design and monitoring approaches, you bring predictability and transparency to production workloads, creating resilience and efficiency at scale. This learning ensures that you not only deploy systems but also monitor and adjust them successfully over time.
Domain 6: Testing and Deployment (10% of the exam)
Testing & Deployment
- Adapt a notebook dependency pattern to use Python file dependencies
- Adapt Python code maintained as Wheels to direct imports using relative paths
- Repair and rerun failed jobs
- Create Jobs based on common use cases and patterns
- Create a multi-task job with multiple dependencies
- Design systems that control for cost and latency SLAs for production streaming jobs
- Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters
- Execute commands from the CLI to deploy and monitor Databricks jobs
- Use REST API to clone a job, trigger a run, and export the run output
Testing & Deployment summary: This section equips you to integrate reliability and efficiency into development and deployment workflows. Building reusable Python package structures and managing imports across wheels and files strengthens maintainability. At the same time, knowledge of how to repair, rerun, and adapt jobs ensures uninterrupted productivity and confidence in production stability.
Deployments extend beyond the UI, reinforcing automation through the Databricks CLI and REST API for end-to-end job lifecycle management. Combined with creating multi-task jobs and ensuring alignment with service-level expectations, these practices help scale deployments while keeping quality at the forefront. The result is streamlined, efficient delivery pipelines that support large-scale, production-ready workloads.
Who benefits most from the Databricks Certified Data Engineer Professional certification?
The Databricks Certified Data Engineer Professional certification is designed for individuals who want to validate advanced hands-on expertise in data engineering within the Databricks Lakehouse Platform. This credential is excellent for:
- Experienced data engineers looking to showcase their ability to build optimized, reliable, and secure data pipelines.
- Professionals working heavily with Apache Spark, Delta Lake, and structured streaming.
- Engineers who manage production-grade ETL pipelines and require deep skills in performance tuning, security, monitoring, and governance.
- Teams and managers who rely on Databricks for mission-critical analytics and want a trusted indicator of skill.
Ultimately, if you’re already comfortable building and maintaining sophisticated data workflows and want to solidify your position as a top-tier Databricks data engineer, this certification is a strong career move.
What types of job opportunities can this certification unlock?
Earning the Databricks Certified Data Engineer Professional certification demonstrates that you are capable of managing advanced workloads in one of the industry’s fastest-growing platforms. Job opportunities often include roles such as:
- Senior Data Engineer
- Big Data Engineer
- ETL Engineer focused on Spark and Databricks
- Data Platform Engineer
- Machine Learning Data Engineer (connecting ML workflows with production pipelines)
- Cloud Data Engineer with a specialization in lakehouse architecture
Employers seek professionals who can optimize streaming and batch pipelines, enforce governance policies, and design scalable solutions for real business needs. Certified professionals often find themselves in leadership positions or as subject matter experts guiding cloud data modernization initiatives.
How long is the Databricks Data Engineer Professional exam?
The exam length is 120 minutes. This gives candidates a well-balanced amount of time to carefully read through scenario-based questions, review code snippets, and apply real-world knowledge. While pacing yourself is important, two hours is a generous timeframe that allows you to demonstrate both speed and depth of understanding without feeling rushed.
How many questions appear on the certification exam?
The exam has a total of 60 multiple-choice questions. The questions are written to test a mix of practical application, conceptual depth, and familiarity with Databricks tools and APIs. Some items may not count toward your score, as unscored questions are included for research purposes, but these are not identified during the test.
What is the required passing score?
To achieve the certification, you need a score of 70 percent or higher. This ensures that certified professionals hold a strong grasp of the Databricks Lakehouse ecosystem. The scoring system evaluates performance across all domains, so even if you are stronger in one area and weaker in another, your overall score is what determines whether you pass.
How much does the exam cost?
The registration fee for the Databricks Certified Data Engineer Professional exam is $200 USD. Additional taxes may apply depending on your country. Many organizations consider certification costs an investment in professional development, so it's often worth asking your employer about support or reimbursement.
What languages is the exam offered in?
Currently, the exam is offered in English. Since code samples focus on Python with Delta Lake SQL references, being comfortable with English technical documentation is important for success.
What is the exam code and version?
The exam uses the latest version recognized by Databricks. While versions may evolve to reflect platform updates, registering through Databricks guarantees you are taking the current and valid exam. Always check the official page before scheduling to be certain you are preparing for the right version.
How is the exam delivered?
The test is delivered as an online proctored exam. This means you can take it from your home or office as long as your environment meets the technical requirements. A webcam, quiet space, and reliable internet connection are required. The proctor ensures exam integrity while giving you the convenience of remote access.
What are the major domains for the exam and their weightings?
The certification blueprint is divided into six main knowledge domains:
- Databricks Tooling (20 percent)
- Data Processing (30 percent)
- Data Modeling (20 percent)
- Security and Governance (10 percent)
- Monitoring and Logging (10 percent)
- Testing and Deployment (10 percent)
These weightings highlight the focus on data processing and data modeling, which together make up half the exam. Knowing how to design, implement, and optimize workflows is core to success. Each domain also includes detailed skills like deduplication strategies, SCD implementations, partitioning, governance through dynamic views, and monitoring with the Spark UI.
Do I need experience before attempting the exam?
While there are no formal prerequisites, it is strongly recommended that you have at least one year of hands-on experience with Databricks and its core technologies. Being comfortable with Spark, Delta Lake, and data pipeline orchestration will make your test experience smoother and significantly increase your chances of success.
What training can help me prepare?
Databricks provides both instructor-led and self-paced courses to strengthen your knowledge. The most recommended course is Advanced Data Engineering with Databricks, available in the Databricks Academy. In addition, reviewing the exam guide, practicing hands-on coding, and regularly exploring Databricks documentation are excellent preparation strategies.
How long does the certification remain valid?
Your certification will remain valid for 2 years. To maintain your certification status, Databricks requires recertification by retaking the current version of the exam. This ensures that certified professionals stay aligned with new platform enhancements and best practices.
Does the exam include unscored questions?
Yes, the exam may include a small number of unscored items. These questions are used for future test development and do not affect your result. Since these items are not identified, it is important to give each question your full attention.
What format of questions should I expect?
The test is composed of multiple-choice questions. Some questions are straightforward, while others present real-world data scenarios. You may also see pseudo-code or SQL queries where you must interpret the correct outcome. Because of the applied nature of data engineering, expect to be tested on practical problem-solving rather than just memorization.
What coding languages are used in the exam?
Most of the code examples provided in the exam are written in Python, since PySpark is widely adopted across data engineering teams. However, all Delta Lake operations are specified in SQL, making it important to feel confident in both areas when preparing for the test.
What topics should I prioritize in my study plan?
Key topics to focus on for exam success include:
- How Delta Lake manages ACID transactions and concurrency control
- Partitioning strategies and optimizations (z-ordering, bloom filters, small file handling)
- Structured streaming patterns and change data feed (CDF) design
- Transformation pipelines from bronze to silver to gold
- Data governance through dynamic views and access controls
- Monitoring pipelines with Spark UI and troubleshooting performance bottlenecks
- Deployment methods with the Databricks CLI and REST API
Mastery of these subjects not only prepares you for the exam, but also sharpens your real-world workflows.
Is the Databricks Certified Data Engineer Professional exam considered advanced?
Yes. This certification is intended for engineers already proficient with Databricks. It validates the ability to design optimized systems that move seamlessly from ingestion to transformation and governance. While advanced, it is still approachable for those who have real-world pipeline experience and who dedicate focus to the specific domains outlined in the exam guide.
How does this certification compare to the Databricks Associate-level exam?
The Databricks Certified Data Engineer Associate exam validates foundational skills, while the Professional-level certification assesses advanced proficiencies such as change data capture, real-time processing, pipeline monitoring, and applying governance at scale. Successfully earning the Professional-level credential signals to employers that you can tackle enterprise-grade data challenges.
What are the next steps after earning this credential?
Once you’re certified, you can expand into other Databricks specializations or even pursue cloud certifications from providers such as AWS, Azure, or Google Cloud to complement your data engineering expertise. You may also consider machine learning certifications, since Databricks integrates tightly with ML and AI workflows.
What’s the best way to practice before taking the test?
The most effective strategy is to combine hands-on Databricks projects with practice exams. Taking top-quality Databricks Certified Data Engineer Professional practice exams allows you to simulate the real testing environment, measure your readiness across all domains, and learn from detailed answer explanations.
Where can I officially register for the Databricks Certified Data Engineer Professional exam?
You can view the official details and schedule your exam directly through the Databricks Certified Data Engineer Professional certification page. This ensures you are accessing the most current information and registering through the trusted Databricks portal.
The Databricks Certified Data Engineer Professional certification is a career-defining milestone for data engineers who want to establish authority in the lakehouse domain. With structured preparation, hands-on practice, and the right mindset, you will be equipped to earn a prestigious credential that elevates your professional standing and opens new opportunities in the world of modern data engineering.
