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Databricks Certified Generative AI Engineer Associate Quick Facts
The Databricks Certified Generative AI Engineer Associate certification opens the door to mastering the skills needed to design, build, and manage generative AI applications with confidence. This overview gives you a clear roadmap of the exam domains, helping you focus on the knowledge and practical expertise that will set you up for success.
How does the Databricks Generative AI Engineer Associate certification empower your career?
This certification validates your ability to design, develop, deploy, and monitor generative AI applications using Databricks tools and foundational machine learning concepts. It is geared toward practitioners, engineers, and technologists who want to demonstrate their capabilities in building real-world solutions such as retrieval augmented generation (RAG) apps, LLM-powered workflows, and scalable generative AI deployments. With a strong emphasis on practical application, this credential ensures you can translate business requirements into effective AI solutions while applying best practices in governance, monitoring, and cost efficiency.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: Design Applications (14% of the exam)
Section 1: Design Applications
- Design a prompt that elicits a specifically formatted response
- Select model tasks to accomplish a given business requirement
- Select chain components for a desired model input and output
- Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline
- Define and order tools that gather knowledge or take actions for multi-stage reasoning
Section 1 summary: This section emphasizes the art of shaping generative AI solutions from an initial concept into a structured design. You are expected to think critically about how prompts, models, and components translate into business outcomes. That includes selecting appropriate tasks for a given requirement, designing prompts that elicit responses aligned with format or style constraints, and mapping use case goals into pipeline inputs and outputs that are both practical and measurable.
The ability to reason about chains and multi-stage workflows is also central here. You will define tools that extend a model’s reasoning abilities, selecting and sequencing them so that each step contributes value to the final outcome. By practicing this structured approach, you will build the habits necessary to design intelligent, modular, and scalable applications that serve specific end-user objectives.
Domain 2: Data Preparation (14% of the exam)
Section 2: Data Preparation
- Apply a chunking strategy for a given document structure and model constraints
- Filter extraneous content in source documents that degrades quality of a RAG application
- Choose the appropriate Python package to extract document content from provided source data and format
- Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog
- Identify needed source documents that provide necessary knowledge and quality for a given RAG application
- Identify prompt/response pairs that align with a given model task
- Use tools and metrics to evaluate retrieval performance
Section 2 summary: This section focuses on the critical foundation of generative AI applications: data preparation. Exam questions will explore your ability to segment content through effective chunking strategies, ensuring large documents are represented in ways that align with model constraints and retrieval accuracy. You will also need to determine which data is most valuable for application success, filtering out extraneous information while retaining knowledge sources of the highest quality.
Another vital part of this domain is integrating cleanly prepared data into Databricks. You’ll create pipelines to load chunked text into Delta Lake, manage storage through Unity Catalog, and choose the right tools for document parsing. Your ability to map prompt pairs to specific tasks and apply retrieval performance metrics ensures that your data pipeline not only operates smoothly but also maximizes downstream model accuracy and relevance.
Domain 3: Application Development (30% of the exam)
Section 3: Application Development
- Create tools needed to extract data for a given data retrieval need
- Select Langchain or similar tools for use in a Generative AI application
- Identify how prompt formats can change model outputs and results
- Qualitatively assess responses to identify common issues such as quality and safety
- Select chunking strategy based on model and retrieval evaluation
- Augment a prompt with additional context from user input based on key fields, terms, and intents
- Create a prompt that adjusts an LLM's response from a baseline to a desired output
- Implement LLM guardrails to prevent negative outcomes
- Write metaprompts that minimize hallucinations or leaking private data
- Build agent prompt templates exposing available functions
- Select the best LLM based on the attributes of the application to be developed
- Select an embedding model context length based on source documents, expected queries, and optimization strategy
- Select a model from a model hub or marketplace for a task based on model metadata and model cards
- Select the best model for a given task based on common metrics generated in experiments
Section 3 summary: In this domain, your focus is building and refining applications that bring generative AI capabilities to life. You’ll select the right libraries or frameworks, such as LangChain, then learn how prompts, embedding models, and evaluation strategies work together to create high-impact outcomes. Understanding how small changes in prompts affect outputs, and how to introduce context into queries, ensures you can deliver precise results even in complex retrieval and reasoning scenarios.
Moreover, governance and safety are embedded within development practices at this stage. You will design guardrails to reduce risks such as hallucinations or data leakage and practice the art of drafting metaprompts to steer models reliably. Application development also includes making evidence-driven model selection decisions, using metrics from experiments to guide your strategy. This reinforces not just technical expertise but also a strong product-development mindset for building trustworthy AI applications.
Domain 4: Assembling and Deploying Applications (22% of the exam)
Section 4: Assembling and Deploying Applications
- Code a chain using a pyfunc model with pre- and post-processing
- Control access to resources from model serving endpoints
- Code a simple chain according to requirements
- Code a simple chain using Langchain
- Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature
- Register the model to Unity Catalog using MLflow
- Sequence the steps needed to deploy an endpoint for a basic RAG application
- Create and query a Vector Search index
- Identify how to serve an LLM application that leverages Foundation Model APIs
- Identify resources needed to serve features for a RAG application
Section 4 summary: This domain is all about transforming well-designed models and data pipelines into working solutions deployed at scale. You will gain practical experience coding chains, registering models, and managing dependencies required to assemble a complete RAG application. A key emphasis is understanding the role of Unity Catalog, MLflow tracking, and Vector Search in organizing and enabling discoverable, performant deployments.
Deployment also extends to security and operational considerations. You will sequence steps for serving endpoints, control access to critical resources, and configure APIs to integrate applications with larger systems. By mastering these approaches, you ensure reliable performance and create a streamlined path from prototype to production, ready to serve users within enterprise environments.
Domain 5: Governance (8% of the exam)
Section 5: Governance
- Use masking techniques as guardrails to meet a performance objective
- Select guardrail techniques to protect against malicious user inputs to a Generative AI application
- Recommend an alternative for problematic text mitigation in a data source feeding a RAG application
- Use legal or licensing requirements for data sources to avoid legal risk
Section 5 summary: Governance represents the ethical and compliance dimension of generative AI solutions. This section assesses your ability to design guardrails that protect applications from potential misuse, both in terms of technical performance and in response to user-generated content. Techniques like masking inputs, filtering malicious data, and mitigating problematic text sources ensure applications remain resilient and aligned with organizational standards.
Additionally, governance extends to legal and licensing responsibilities. You’ll need to demonstrate how to select and validate safe data sources, applying knowledge of intellectual property and licensing requirements to avoid risk. By incorporating governance into your practice, you not only build better AI models but also foster trust, compliance, and long-term sustainability across use cases.
Domain 6: Evaluation and Monitoring (12% of the exam)
Section 6: Evaluation and Monitoring
- Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
- Select key metrics to monitor for a specific LLM deployment scenario
- Evaluate model performance in a RAG application using MLflow
- Use inference logging to assess deployed RAG application performance
- Use Databricks features to control LLM costs for RAG applications
Section 6 summary: This final domain highlights the importance of continuous improvement and operational visibility in generative AI applications. You’ll use quantitative metrics to select the most suitable model, considering architecture and size as important factors tied to workload demands. In addition, you will practice tracking both system and model outcomes, identifying what metrics indicate success across different stages of deployment.
Evaluation does not stop at testing; it extends to monitoring in production. Leveraging inference logging, MLflow, and Databricks cost management tools allows you to optimize both efficiency and expense. By prioritizing robust monitoring practices, you ensure that applications remain effective, scalable, and financially sustainable while providing actionable insights for iterative enhancements.
Who should consider the Databricks Certified Generative AI Engineer Associate Certification?
The Databricks Certified Generative AI Engineer Associate Certification is an excellent fit for individuals who want to demonstrate practical skills in designing and implementing LLM-enabled solutions. It is particularly valuable for:
- Data and machine learning professionals who want to upskill into the generative AI domain
- Software engineers looking to build RAG (retrieval-augmented generation) applications
- Cloud practitioners working with AI and ML teams in real-world projects
- AI enthusiasts seeking their first industry-recognized credential in generative AI engineering
This certification is built for doers: people excited about applying large language models (LLMs) and cutting-edge AI tools to solve business challenges on the Databricks platform.
What roles or career opportunities does this Databricks Generative AI certification unlock?
Earning this certification not only shows employers you understand generative AI, but also positions you for exciting career roles. Professionals often pursue this to move into or advance in roles like:
- Generative AI Engineer
- Machine Learning Engineer specializing in LLMs
- AI Solutions Developer
- Cloud AI Application Developer
- Data Scientist focused on RAG applications
- Applied AI/ML Specialist for enterprise solutions
With companies increasingly investing in generative AI and LLM-powered pipelines, this certification signals that you can deliver scalable, production-ready AI applications.
What is the Databricks Certified Generative AI Engineer Associate exam format?
The exam format reflects what you need to succeed in the real world. The test includes 45 multiple-choice questions to be completed within 90 minutes. Every question is designed to gauge your ability to design, build, deploy, and monitor AI applications using the Databricks ecosystem.
There are no trick questions. Instead, the focus is on problem-solving within generative AI. All the exam code samples and questions are oriented around Python, with some supporting SQL for data operations.
What is the passing score for the Generative AI Engineer Associate exam?
To earn your credential, you’ll need to reach a passing score of 70 out of 100. Think of this as demonstrating proven competence across all sections, rather than acing every single area. The exam uses a balanced scoring approach, which means your overall total matters more than individual sections.
By focusing on a well-rounded understanding of data preparation, application design, development, and deployment, you’ll set yourself up to comfortably clear the mark.
How much does the Databricks Certified Generative AI Engineer Associate exam cost?
The exam fee is 200 USD, plus applicable local taxes. This investment provides a globally recognized validation of your generative AI and Databricks skills, creating opportunities across industries.
If you consider the salary boost of AI engineering careers, this certification quickly pays for itself, giving you both credibility and an edge in the job market.
How long is the Databricks Generative AI Engineer Associate exam?
You’ll have 90 minutes to complete all questions in the exam. While this timeframe is generous for 45 questions, it’s important to manage your time well. Longer scenario-based questions may require a bit more mental unpacking, while some direct technical items may be quicker.
Most candidates find that the time is enough if they pace themselves evenly without lingering too long on individual questions.
What languages is the exam available in?
The exam is designed for a global audience and is available in several of the world’s most widely spoken languages. You can take it in:
- English
- 日本語 (Japanese)
- Português (Brazilian Portuguese)
- 한국어 (Korean)
This makes the exam accessible whether you’re in North America, Asia, South America, or beyond.
What are the main exam domains and their weightings?
The exam content is thoughtfully distributed across six domains, reflecting all stages of generative AI application building. Here’s the breakdown:
- Design Applications (14%) – Shaping prompts, chaining models, and aligning outputs to business goals
- Data Preparation (14%) – Chunking, extraction, filtering, and preparing data for RAG applications
- Application Development (30%) – Building prompts, agents, LLM guardrails, and selecting models
- Assembling and Deploying Apps (22%) – Deploying chains, integrating with Unity Catalog, Vector Search, and MLflow
- Governance (8%) – Managing risks, guardrails, compliance, and data governance
- Evaluation and Monitoring (12%) – Leveraging Databricks to track, evaluate, and optimize deployed AI applications
With Application Development (30%) carrying the heaviest weighting, it’s wise to prioritize real-world practice in chaining, prompt engineering, and development workflows.
How long does the Databricks certification remain valid?
Once you pass, your certification remains valid for 2 years. After this period, you’ll need to recertify by taking the current version of the exam to maintain active certified status.
This ensures your skills remain current with the latest Databricks capabilities and evolving generative AI trends—a valuable commitment to professional relevancy.
Is there an official exam code for this certification?
Yes! The most current exam is referred to as the Latest Version of the Databricks Certified Generative AI Engineer Associate exam. Databricks keeps the name streamlined, so you’ll always be working on the up-to-date exam version when you register.
Are there any required prerequisites?
There are no formal prerequisites, meaning anyone can register and attempt the exam. However, Databricks strongly recommends at least 6 months of hands-on experience in generative AI solution development before sitting for the certification.
This experience helps you apply theory to real problems, giving you the confidence to navigate questions with practical insight.
What technical knowledge should I master before the exam?
The exam blends Databricks-specific expertise with core generative AI knowledge. You should be familiar with:
- LLMs and their capabilities
- Prompt engineering and evaluation techniques
- Tools like LangChain and Hugging Face Transformers
- Python development for AI workflows
- Data extraction, transformation, and loading (ETL) into Delta Lake with Unity Catalog
- Model Serving, Vector Search, MLflow lifecycle management
By mastering these areas, you’ll cover nearly every question type expected on the exam.
What practical Databricks tools should I expect to see on the exam?
This exam places an emphasis on real Databricks tools. Expect to work with and reason about:
- Databricks Vector Search for semantic matching
- Model Serving for deploying scalable AI models
- Unity Catalog for governance and secure data management
- MLflow for model training, tracking, and lifecycle control
These tools are central to building enterprise-grade AI applications on Databricks.
What are common mistakes test takers should avoid?
The most common trip-ups come from skipping hands-on practice. Candidates sometimes only study theory without trying the tools, but practical understanding is key. Another mistake is overlooking governance topics like ethical AI, compliance, and guardrails—these do carry weight on the exam.
A smart approach is balancing technical deep dives with time spent actually chaining prompts, deploying sample models, and tracking runs with Databricks features.
How difficult is this exam compared to other AI certifications?
This certification offers a solid associate-level scope. Unlike research-heavy AI certifications, the focus here is applied engineering. You’ll find it very approachable if you’ve done some practice with Python-based LLM frameworks and worked inside Databricks.
Think of it as your entry ticket to production-grade generative AI roles, bridging the gap between theory and engineering.
Where do I register for the Databricks Certified Generative AI Engineer Associate exam?
You register directly on the official Databricks Certified Generative AI Engineer Associate certification page. From there, you can select your preferred date and delivery option.
What delivery methods are available for the exam?
The certification exam is offered exclusively as an online proctored exam, meaning you can take it from the comfort of your home or office while a proctor ensures exam integrity.
A quiet environment, stable internet connection, and a webcam are the only requirements.
How many attempts are allowed?
If you don’t achieve the passing score the first time, Databricks allows retakes after a designated waiting period. Each attempt requires a separate exam fee. Always double-check the official retake policy before booking.
Fortunately, strong preparation greatly reduces the need for multiple attempts.
What’s the best way to prepare for the Databricks Generative AI exam?
Preparation works best when theory meets hands-on practice. Recommended prep methods include:
- Completing Databricks Academy’s Generative AI Engineering courses
- Reviewing Databricks documentation and LLM integration tutorials
- Practicing with Python for creating prompt pipelines and RAG applications
- Using structured study tools like Databricks Certified Generative AI Engineer Associate practice exams that simulate the real test and guide you with detailed explanations
This blended approach ensures you understand not just what to do, but why certain approaches work best.
How does this certification help me stand out to employers?
Employers know that Databricks is a leader in enterprise AI, and this certification signals more than theoretical exposure—it verifies your ability to drive real solutions. With businesses clamoring for generative AI engineers, adding this badge to your profile sets you apart as someone ready to deliver.
It also communicates that you thrive with both cutting-edge ML models and governance best practices, which is a rare and valuable combo.
How does the Databricks Certified Generative AI Engineer Associate fit into a career roadmap?
This credential is often a mid-point stepping stone. Many earn it while transitioning from roles like data engineer or analyst into specialist AI development. From here, you can pursue advanced certifications in machine learning with Databricks or expand into broader ML engineering certifications.
It’s both a strong standalone credential and a launchpad into deeper AI engineering mastery.
The Databricks Certified Generative AI Engineer Associate certification is a powerful investment in your AI future. By preparing strategically, practicing hands-on, and leveraging Databricks’ rich ecosystem, you’ll not only pass the exam but also unlock opportunities to shape the future of AI applications. Get started by registering through the official Databricks certification page and showcase your skills with confidence.
