Databricks runs 7 certifications, 5 at the Associate level and 2 at the Professional level, and every single one costs $200, requires a 70 percent passing score, and stays valid for 2 years, making cost and format the least useful factors when deciding which one to take first. Official exam guides and registration live on Databricks’ certification page.
The direct answer: Start with Data Engineer Associate if you’re new to the platform; it’s the most commonly held Databricks certification and the one most study paths assume you’ll take first. Move to Machine Learning Associate or Data Analyst Associate if your work leans ML or SQL-based analytics specifically. Generative AI Engineer Associate is currently the fastest-growing certification in the catalog, reflecting how quickly organizations are building production GenAI applications on lakehouse architectures. Professional-level exams assume roughly a year of hands-on production experience and shouldn’t be your entry point.
The Full Catalog
| Certification | Level | What It Validates | Cost |
| Data Engineer Associate | Associate | Core ETL with Spark SQL and Python, Delta Lake, Unity Catalog, Workflows | $200 |
| Data Analyst Associate | Associate | Databricks SQL, dashboards, data visualization, Unity Catalog | $200 |
| Machine Learning Associate | Associate | End-to-end ML workflows using AutoML, Feature Store, MLflow, Spark ML | $200 |
| Generative AI Engineer Associate | Associate | Building and deploying GenAI solutions including RAG pipelines and LLM fine-tuning | $200 |
| Associate Developer for Apache Spark | Associate | Vendor-neutral Spark DataFrame API and architecture skills | $200 |
| Data Engineer Professional | Professional | Advanced design, optimization, governance, monitoring, and deployment | $200 |
| Machine Learning Professional | Professional | Advanced model development, MLOps, and controlled deployment | $200 |
Important: Associate Developer for Apache Spark is genuinely different from the other Associate exams. It tests core Spark programming concepts and architecture that apply on AWS EMR, Azure HDInsight, Google Dataproc, or any Spark environment, not Databricks-specific features. If your goal is Databricks-specific expertise, this credential proves something narrower than its name suggests. For the full breakdown of how this exam relates to the more Databricks-focused entry point, see our Apache Spark Developer vs Data Engineer Associate guide.
Format, Across Every Exam
| Detail | Associate Exams | Professional Exams |
| Cost | $200 | $200 |
| Question count | 45 | 59 to 60 |
| Duration | 90 minutes | 120 minutes |
| Passing score | 70 percent | 70 percent |
| Validity | 2 years | 2 years |
| Question format | Multiple choice and scenario-based | Multiple choice and scenario-based |
| Hands-on lab or drag-and-drop components | None | None |
| Delivery | Online proctored via Webassessor, Kryterion, or a physical test center | Online proctored via Webassessor, Kryterion, or a physical test center |
| Language availability | 5 exams available in Japanese | Generative AI Engineer and Apache Spark Developer are currently English-only |
Note: Every Associate exam runs 45 questions in 90 minutes, and every Professional exam runs 59 to 60 questions in 120 minutes, roughly 2 minutes per question either way. This format is identical across the entire catalog, so once you understand the pacing on one exam, you understand it for all 7. Databricks updates exam blueprints periodically, not on a fixed public schedule, if you’re studying from materials more than a year old, confirm the current blueprint against Databricks’ own certification page before assuming the content still matches.
Domain Weighting, Exam by Exam
Data Engineer Associate
| Domain | Weight |
| ELT with Spark SQL and Python | 29 percent |
| Databricks Lakehouse Platform | 24 percent |
| Incremental Data Processing | 22 percent |
| Production Pipelines | 16 percent |
| Data Governance | 9 percent |
Generative AI Engineer Associate
| Domain | Weight |
| Application Development | 30 percent |
| Assembling and Deploying Applications | 22 percent |
| Design | 14 percent |
| Data Preparation | 14 percent |
| Evaluation and Monitoring | 12 percent |
| Governance | 8 percent |
Machine Learning Professional
| Domain | Weight |
| Model Development | 44 percent |
| ML Ops | 44 percent |
| Model Deployment | 12 percent |
Associate Developer for Apache Spark
| Domain | Weight |
| Spark DataFrame API | 30 percent |
| Spark Architecture | 20 percent |
| Spark SQL | 20 percent |
| Structured Streaming, Spark Connect, and tuning | Remaining 30 percent |
Key point: Machine Learning Professional’s near-even 44/44 split between Model Development and ML Ops is unusual among certification exams generally, and it’s a big part of why multiple 2026 study guides describe it as the hardest exam in the entire Databricks catalog. There’s no single dominant domain to concentrate study time on, and genuine weakness in either half costs nearly half the available points. For the complete comparison against the Associate-level version of this exam, see our Machine Learning Associate vs Professional guide.
What Each Certification Actually Tests
Databricks Certified Associate
Data Engineer Associate validates introductory data engineering: building ETL pipelines that move between Spark SQL and Python, handling incremental batch and streaming data, orchestrating pipelines through Databricks Workflows, and basic governance through Unity Catalog. It’s the most commonly held Databricks certification and the default starting point most study paths recommend. If you’re also weighing whether to go deeper on this track or pivot toward analytics instead, see our Data Engineer Associate vs Data Analyst Associate comparison.
Data Analyst Associate tests Databricks SQL, dashboards, data visualization, and data management for introductory analytics work. It’s widely described as the most approachable of the 7 exams, requiring no Python or Spark coding, though its SQL and query-optimization content still demands real depth.
Machine Learning Associate validates running a complete traditional ML project inside Databricks: exploring data, engineering features, training and tuning models with AutoML, and tracking experiments through MLflow. It assumes classical ML concepts, classification, regression, forecasting, applied through Databricks-native tooling. If you’re deciding between this track and Databricks’ newer AI-focused certification, see our Machine Learning Associate vs Generative AI Engineer Associate comparison.
Generative AI Engineer Associate is Databricks’ newest certification, launched in 2025, and currently its fastest-growing. It tests hands-on GenAI application development specifically: RAG pipelines, Vector Search, Foundation Model APIs, LangChain-based orchestration, Databricks’ Agent Framework, evaluation methodology, and responsible AI guardrails.
Associate Developer for Apache Spark is the outlier of the group. It tests general Spark DataFrame API skill and Spark architecture, execution modes, fault tolerance, shuffling, the Catalyst Optimizer, that apply to any Spark environment, not Databricks-specific features. It’s valuable precisely because it doesn’t tie your resume to one platform.
Databricks Certified Professional
Data Engineer Professional shifts from “can you build this pipeline” to “can you be trusted to run it in production,” covering performance optimization, security and governance at a granular level, monitoring, and CI/CD for pipeline deployments. For the complete breakdown of this jump, see our Data Engineer Associate vs Professional guide.
Machine Learning Professional covers advanced model development alongside equally-weighted ML Ops responsibilities, monitoring for model drift, managing retraining pipelines, and maintaining reliability, plus a smaller but meaningful Model Deployment domain covering custom serving infrastructure and controlled rollout strategy.
What’s New, and What’s Retired
Databricks’ certification catalog isn’t static. The Databricks Certified Hadoop Migration Architect exam was retired as of August 1, 2024, and is no longer available if you’re researching older material that references it. On the newer end, Databricks has a Context Engineering Associate certification in development, assessing the ability to design, assemble, and govern context for AI agent systems on Databricks specifically, a distinct skill from the RAG and application-building focus of Generative AI Engineer Associate. If you’re planning certification more than a year out, check Databricks’ own certification page for the current state of the catalog, since new specialized exams continue to be added as the platform’s AI capabilities expand.
What Certification Actually Pays
Glassdoor’s 2026 figures put the average Databricks Data Engineer salary at $133,130 a year, with a range from $104,368 up to $171,580 for senior roles. Databricks’ own research, cited across multiple 2026 guides, states certified professionals earn 15 to 30 percent more than non-certified peers in equivalent roles, though that figure comes from the vendor itself rather than an independent source, so treat it as directional rather than an exact promise.
The honest read: Data Engineer Professional and Machine Learning Professional holders land at the higher end of these ranges specifically, consistent with the deeper, production-grade scope those exams test compared to their Associate-level counterparts. Generative AI Engineer Associate holders benefit from a separate demand signal, GenAI-skilled data professionals are consistently ranked among the most in-demand technical hires globally in 2026, even without a precise certification-specific salary figure published yet.
Ways to Reduce What You Pay
| Path | Discount | Eligibility |
| Customer Academy Learning Festival | 50 percent off | Runs roughly twice a year, open enrollment during the window |
| Partner Academy | Varies by partner tier | Employees of active Databricks partner firms, including firms like Deloitte and Accenture |
| Data + AI Summit attendee perks | Varies year to year | Conference attendees, changes annually |
Important: These discount vouchers generally can’t be stacked with each other. If you’re eligible for more than one, pick the single best offer rather than assuming you can combine them.
Choosing Your Starting Point
| Your Background | Start With |
| New to Databricks, coming from a general data or analytics role | Data Engineer Associate |
| SQL-focused, limited Python or engineering background | Data Analyst Associate, the most approachable of the 7 |
| Data science background moving into ML specifically | Machine Learning Associate |
| Building production GenAI applications right now | Generative AI Engineer Associate |
| Already using Spark outside Databricks and want vendor-neutral proof | Associate Developer for Apache Spark |
| 1 or more years of hands-on Databricks production experience | Data Engineer Professional or Machine Learning Professional, depending on your track |
Most tracks expect you to earn the Associate-level credential before attempting Professional, though Databricks doesn’t formally block you from attempting Professional directly if you already have the underlying experience.
For deeper, dedicated comparisons on each of these decision points, see our guides to Data Engineer Associate vs Professional, Data Engineer Associate vs Data Analyst Associate, Machine Learning Associate vs Generative AI Engineer Associate, Machine Learning Associate vs Professional, and Apache Spark Developer vs Data Engineer Associate.
How Databricks Compares to Other Data Platform Certifications
If you’re weighing Databricks against a competing data platform certification, see our Snowflake SnowPro Core Certification Guide for how that ecosystem’s entry point compares in cost and structure. If your data engineering work spans multiple cloud providers rather than Databricks specifically, our AWS Certification Path guide covers AWS’s own Data Engineer Associate credential. And if your GenAI work involves a specific model vendor’s own developer tooling rather than Databricks’ platform-integrated approach, our Claude Certification Path guide covers Anthropic’s equivalent Developer and Architect-level credentials.
Common Mistakes When Planning Your Databricks Path
Assuming Associate Developer for Apache Spark proves Databricks-specific expertise. It tests general Spark programming and architecture skills applicable to any Spark environment, which is valuable but different from what employers screening for Databricks platform experience specifically are looking for.
Attempting Professional-level exams without the roughly year of hands-on experience they assume. Both Professional exams add scenario questions on production patterns, performance, security, and CI/CD, that study materials alone don’t fully prepare you for. Machine Learning Professional specifically has a reputation as the hardest exam in the catalog given its even domain weighting.
Studying from a blueprint more than a year old without checking for updates. Databricks revises exam content periodically, retired the Hadoop Migration Architect exam entirely in 2024, and continues adding new specialized certifications like Context Engineering Associate as the platform evolves.
Assuming certification vouchers stack. Customer Academy, Partner Academy, and conference-based discounts generally don’t combine, so plan around whichever single offer applies to you.
Choosing a certification based on which sounds most technical rather than which matches your actual role. Data Analyst Associate’s SQL focus and Data Engineer Associate’s Python and Spark focus serve genuinely different jobs, and picking based on perceived prestige rather than fit tends to backfire during preparation.
FAQS
Which Databricks certification should I take first?
Data Engineer Associate, the most commonly held Databricks certification and the standard starting point most study paths assume.
How much does a Databricks certification cost?
$200 per exam across all 7 certifications, with periodic discount vouchers available through Databricks’ Customer Academy Learning Festival, Partner Academy, or Data + AI Summit attendance.
Do I need the Associate certification before attempting Professional?
Not formally required, but Professional-level exams assume roughly a year of hands-on production experience that most candidates build through Associate-level work first.
What is the fastest-growing Databricks certification?
Generative AI Engineer Associate, reflecting rapid organizational adoption of production GenAI applications built on lakehouse architectures.
Which Databricks certification is considered the hardest?
Machine Learning Professional, according to multiple 2026 study guides, largely because its Model Development and ML Ops domains are weighted almost identically at 44 percent each, leaving no single dominant area to focus preparation on.
How is Associate Developer for Apache Spark different from the other Associate exams?
It tests vendor-neutral Spark programming and architecture concepts applicable to any Spark environment, not Databricks-specific platform features, making it valuable but narrower in scope than its Associate-level siblings.
How long is a Databricks certification valid?
2 years across all 7 certifications, after which recertification is required.
Do certified Databricks professionals actually earn more?
Databricks’ own research states certified professionals earn 15 to 30 percent more than non-certified peers in equivalent roles, though this figure comes from the vendor rather than an independent source.
What is the passing score and question format for Databricks certifications?
70 percent across all 7 exams. Associate exams run 45 questions in 90 minutes, Professional exams run 59 to 60 questions in 120 minutes, all multiple choice and scenario-based with no drag-and-drop or hands-on lab components.
Has Databricks ever retired a certification?
Yes, the Databricks Certified Hadoop Migration Architect exam was retired as of August 1, 2024. Databricks also continues adding new certifications, including a Context Engineering Associate credential in development for AI agent context design.
