Introduction
To set expectations, this guide walks you through the introduction of the exam: what it is, the topics covered, and if you are interested, some resources to help you prepare for it.
The Databricks Generative AI Engineer Associate is a certification offered by Databricks that helps candidates demonstrate their ability to design and build AI-powered solutions using their platform. Here is the link to the Databricks page to learn more about it:
Databricks Certified Generative AI Engineer Associate | Databricks
Become a Databricks Certified Generative AI Engineer Associate! Prove your skills in designing and implementing LLM-enabled solutions using Databricks' cutting-edge tools.
databricks.com
Here is my certificate for the creditibility and check my score.
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Topics Covered
This exam covers 6 sections, each focusing on different aspects of building and managing generative AI solutions on Databricks. Below is a brief overview of each section along with its percentage weightage.
Design Applications – 14%
As the name suggests, this section focuses on designing various aspects of AI solutions: crafting the right prompt, choosing appropriate models and components, translating business requirements into desired inputs and outputs, and deciding which tools to give the model access to.
Data Preparation – 14%
The first step in creating an AI solution is often preparing the data in a way that helps leverage the model to its full potential. This section includes deciding on chunking strategies, filtering unnecessary data, and choosing the appropriate Python package for extracting data.
Application Development – 30%
This is the most interesting part. It includes creating tools to extract required data, selecting an orchestration framework like LangChain to create chains of prompt templates and models, crafting prompts by augmenting them with retrieved data, implementing guardrails, reducing hallucinations, and selecting the right models and embedding strategies for your use case.
Assembling and Deploying Apps – 22%
Once your application is built, you need to get it out into the world. This section covers deploying model serving endpoints, registering models to Unity Catalog via MLflow, setting up Vector Search indexes, and understanding the end-to-end deployment process for a RAG (Retrieval-Augmented Generation) application.
Governance – 8%
This covers how to protect your application and your data. Think guardrails against malicious inputs, data masking, and staying on the right side of legal and licensing requirements.
Evaluation and Monitoring – 12%
Building it is one thing; making sure it keeps working well is another. This section tests your ability to evaluate model performance, monitor live deployments, control costs, and understand the difference between the evaluation and monitoring phases of the AI application lifecycle.
Exam Overview
The exam consists of 45 scored multiple-choice and multiple-selection questions, though you may encounter a few additional unscored questions. Extra time is factored in to account for these. You will have 90 minutes to complete the exam.
Prerequisites
There are no formal prerequisites required to sit for this exam, which makes it accessible to anyone looking to get started with generative AI on Databricks. That said, Databricks recommends coming in with some solid preparation.
Beyond the coursework, you will want to have a good working knowledge of current LLMs and their capabilities, prompt engineering, and tools like LangChain and Hugging Face Transformers. On the technical side, comfort with Python and its relevant libraries is important, as is familiarity with APIs used for data preparation and model chaining.
Study Plan, Resources, and Practice Exams
I used Databricks Academy's Generative AI Engineering Pathway to prepare for the certification. It consists of 6 courses with a total duration of 10 hours and 35 minutes. I personally took almost a week (including a long weekend) to complete this course. Upon completing each course, you also earn a Databricks badge that you can share. Each course also includes a tutorial walkthrough of the explained topic. Since I have worked on these AI topics before, I did not follow the tutorials hands-on, but depending on your experience level, you may want to. Upon completing the pathway, you will receive a coupon code via email after a few days that you can use to book the exam for free.
After completing the pathway, it was time to take some practice exams. Since this is a fairly new certification, there are limited resources available online. After some research, I decided to buy practice questions from SkillCertPro. This package contains around 670 questions across 12 mock exams. There were questions in the actual exam that overlapped with these practice tests, so I highly recommend getting this one. (Not sponsored!)
Databricks Generative AI Engineer Associate Practice Questions 2026
Databricks Generative AI Engineer Associate Exam Questions 2026. Prepare and pass in first attempt. with valid dumps. 100% real exam questions
skillcertpro.com
After completing 1–2 practice exams, I booked my (as always, in-person) exam for the following week. During that week, I worked through one practice exam per day, reviewed the explanations for wrong answers, and used Claude to dig deeper into concepts I wasn't sure about.
I also came across a couple of blogs that really helped with preparation:
- https://medium.com/@chandadipendu/databricks-generative-ai-engineer-associate-certification-study-guide-part-1-70cf3c483085
- https://www.linkedin.com/pulse/preparation-guide-databricks-generative-ai-engineer-associate-mane-0toxf
Beyond these, depending on your weak domains, Databricks also publishes topic-specific blogs that can help deepen your understanding.
My Score
Design Applications: 80%
Data Preparation: 57%
Application Development: 71%
Assembling and Deploying Apps: 100%
Governance: 100%
Evaluation and Monitoring: 80%
Conclusion
To wrap up, most of the exam questions were not just Databricks-specific, but covered real-world Generative AI engineering concepts. So while preparing, make sure you also understand the core concepts of GenAI beyond the Databricks platform. This will help you build transferable skills that go beyond the exam itself.
Best of luck! Let me know if you have any further questions, and don't forget to let me know once you pass.
Top comments (1)
Glad to share that I’ve passed the Databricks Generative AI Engineer exam on my second attempt. This time I used Passexamhub, and honestly around 90% of the questions in the exam were from there. The content was updated and felt very close to the real exam. Definitely helped me get through this time.