As part of my journey in learning machine learning development and operations, I decided to study for the Databricks Machine Learning Associate Exam. While the certification itself was valuable, I found the learning process to be even more rewarding. The exam tests your ability to perform machine learning operations on the Databricks platform, but much of the material has broader applications. Core topics like model building, training, evaluation and deployment are fundamental to machine learning operations across all platforms, not just Databricks. This universal applicability was the true value I gained from my study experience.
Exam Information
The exam content is straightforward. It tests your ability to perform basic machine learning tasks on Databricks—some platform-specific and others broadly applicable to machine learning. About 60% of the exam covers general machine learning tasks, while 40% focuses on Databricks-specific implementations. If you're already comfortable with concepts like supervised vs. unsupervised learning, hyperparameter tuning, and evaluation metrics, you'll find the exam preparation manageable. While I was familiar with these topics, reviewing them during exam prep deepened my understanding significantly. Even if you're less confident in these areas, the exam preparation materials will guide you effectively. The Databricks-specific topics include AutoML, MLflow, feature store tables, and Model Lifecycle. These areas require hands-on practice with notebooks and exploration of the Databricks UI.
Here's a breakdown of the exam topics by percentage:
Databricks Machine Learning — 38%
Model Development — 31%
ML Workflows — 19%
Model Deployment — 12%
Study Process
Similar to my study process for the Databricks Engineer Professional, I used the Databricks Learning Academy and Udemy as my learning sources. Looking at the exam information on the Databricks website, you'll notice the self-paced training is now divided into four courses. This is a recent update—previously, all content was taught in a single learning path. The Databricks team restructured it, presumably to create a clearer learning pathway. The courses provide excellent theory and code examples, with hands-on labs at the end of each topic. While you used to be able to download the notebook examples for practice, Databricks has unfortunately removed this feature across all their training courses during the recent update. Despite this limitation, you can still recreate your own versions of the labs by following along with the video demonstrations.
If you don't have access to the Databricks Learning Academy, Udemy courses and YouTube content can effectively help you understand the material. Whether you choose Databricks or Udemy as your learning source, you'll need to take practice exams to validate your understanding. There are plenty of mock exams that you can find on Udemy that will test your knowledge.
Topics Breakdown
Databricks Machine Learning (38%)
This section covers machine learning workflows that integrate with the Databricks ecosystem. MLOps, AutoML, Feature Stores, and MLflow are the key topics you need to understand. While it's important to grasp the overall significance of these areas before diving into syntax, the exam does require specific syntax knowledge for Feature Stores and MLflow. Though I found it odd that the exam emphasized memorizing syntax over conceptual understanding, the required syntax wasn't overly complex. Pay special attention to workflows involving Feature Stores (including their syntax) and how MLflow integrates with Unity Catalog. Make sure to understand the syntax behind registering models, setting tags and model promotion. I encountered several tricky questions about these topics on the exam. From my experience, AutoML, Feature Stores, and MLflow were the most valuable components of this section for the exam.
Model Development (31%)
This section covers general machine learning development concepts. You should understand the fundamentals of supervised vs. unsupervised learning, hyperparameter tuning, and evaluation metrics such as Root Mean Squared Error and F1 score. During the exam, I encountered several questions about selecting appropriate evaluation metrics for specific purposes. If you're already familiar with machine learning model development, this section will be simple. However, there are some specific technical distinctions the exam will expect you to understand, such as the difference between an estimator and transformer, and knowing when to exponentiate log-transformed variables.
ML Workflows (19%)
This section of the exam focuses on data processing in machine learning workflows, particularly feature engineering and its related processes. Pay special attention to imputation methods, log scale transformations, and the comparison of categorical versus continuous features. This section is also agnostic to the Databricks platform. Since this content is platform-agnostic—meaning it's not specific to Databricks—you can study it without accessing the Databricks environment. Compared to other sections, this one should be relatively easy to understand.
Model Deployment (12%)
The final section covers deployment methods available on Databricks. The exam includes questions about batch, streaming, and real-time deployment—you'll need to understand the differences between these approaches. I do remember some questions on the exam that touched this topic. Understanding MLflow flavors is also valuable, particularly if you're planning to take the Professional exam, where this topic is explored in greater depth. Additionally, familiarize yourself with Model Serving, including how to query endpoints and split traffic across multiple endpoints.
Personal Study Guide
I've created a personal study guide for this exam, which you can access here. The guide contains notes that directly align with the exam content and includes coding examples from various sources. While it's not comprehensive, you can supplement it with your own notes where needed.
Summary
This certification was engaging to study for, thanks to its versatile content. I gained valuable knowledge about practical machine learning development and deployment. Obviously the content is geared toward performing these actions on the Databricks platform. I believe Databricks offers a good solution for machine learning applications and I can’t wait to test them out some more. However, the material you learn in this exam can be performed in many different ways and on different platforms. The Professional exam expands on these concepts, offering deeper coverage of deployment and operations—I'm excited to discuss my experience with that exam soon.
Resources
Databricks Certified Data Engineer Associate Practice Exams
Databricks Learning Academy
what Udemy practice exams you used? were they helpful?