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Databricks Certified Machine Learning Associate Quick Facts
The Databricks Certified Machine Learning Associate is a powerful way to demonstrate your ability to design, build, and deploy machine learning workflows using Databricks. This overview provides clear guidance and structure, helping you move forward with confidence as you prepare for success.
How does the Databricks Certified Machine Learning Associate boost your skills and career?
This certification validates your ability to process data, develop ML models, and apply MLOps best practices directly within the Databricks platform. It highlights your capability to work with feature stores, MLflow, AutoML, and modern deployment techniques. Whether you are preparing to advance in data science, machine learning engineering, or applied analytics roles, the certification demonstrates your applied proficiency in building robust ML solutions.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: Databricks Machine Learning (38% of the exam)
Databricks Machine Learning
- Identify the best practices of an MLOps strategy
- Identify the advantages of using ML runtimes
- Identify how AutoML facilitates model/feature selection
- Identify the advantages AutoML brings to the model development process
- Identify the benefits of creating feature store tables at the account level in Unity Catalog in Databricks vs at the workspace level
- Create a feature store table in Unity Catalog
- Write data to a feature store table
- Train a model with features from a feature store table
- Score a model using features from a feature store table
- Describe the differences between online and offline feature tables
- Identify the best run using the MLflow Client API
- Manually log metrics, artifacts, and models in an MLflow Run
- Identify information available in the MLflow UI
- Register a model using the MLflow Client API in the Unity Catalog registry
- Identify benefits of registering models in the Unity Catalog registry over the workspace registry
- Identify scenarios where promoting code is preferred over promoting models and vice versa
- Set or remove a tag for a model
- Promote a challenger model to a champion model using aliases
Summary: This section emphasizes the end-to-end experience of managing machine learning models using the Databricks platform. You will explore how MLOps strategies streamline collaboration, monitoring, and reuse of models, while learning the role of ML runtimes in providing optimized, consistent environments for training and deployment. AutoML is a key part of this domain, where you will see how automated workflows simplify feature selection and model experimentation, making large-scale machine learning projects more efficient and accessible.
A central part of this domain is the Unity Catalog, especially around feature stores and model registries. You will gain practical skills in building feature tables, writing and training with them, and distinguishing between online and offline features. Additionally, you will prepare to manage the model lifecycle with MLflow, from logging runs and metrics to promoting challenger models into champion roles with proper tagging and governance. The skills in this section prepare you to confidently handle both technical execution and strategic process decisions within Databricks.
Domain 2: ML Workflows (19% of the exam)
Data Processing
- Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries
- Remove outliers from a Spark DataFrame based on standard deviation or IQR
- Create visualizations for categorical or continuous features
- Compare two categorical or two continuous features using the appropriate method
- Compare and contrast imputing missing values with the mean or median or mode value
- Impute missing values with the mode, mean, or median value
- Use one-hot encoding for categorical features
- Identify and explain the model types or data sets for which one-hot encoding is or is not appropriate
- Identify scenarios where log scale transformation is appropriate
Summary: This section focuses on preparing, cleaning, and transforming data so that it is ready for machine learning tasks. You will practice techniques for calculating summary statistics, handling outliers, and dealing with missing values, all of which ensure data quality before model development. Visualization and comparison skills between features allow you to better understand datasets and select preprocessing options that align with project goals.
Another key consideration in this section is feature transformation, including one-hot encoding for categorical values and log scale transformations. By learning when and why to use these methods, you will improve model performance and interpretability. This domain aims to establish a practical foundation in preprocessing workflows, providing the tools needed to ensure reliable inputs for machine learning models within Databricks.
Domain 3: Model Development (31% of the exam)
Model Development
- Use ML foundations to select the appropriate algorithm for a given model scenario
- Identify methods to mitigate data imbalance in training data
- Compare estimators and transformers
- Develop a training pipeline
- Use Hyperopt's fmin operation to tune a model's hyperparameters
- Perform random or grid search or Bayesian search as a method for tuning hyperparameters
- Parallelize single node models for hyperparameter tuning
- Describe the benefits and downsides of using cross-validation over a train-validation split
- Perform cross-validation as a part of model fitting
- Identify the number of models being trained in conjunction with a grid-search and cross-validation process
- Use common classification metrics: F1, Log Loss, ROC/AUC, etc.
- Use common regression metrics: RMSE, MAE, R-squared, etc.
- Choose the most appropriate metric for a given scenario objective
- Identify the need to exponentiate log-transformed variables before calculating evaluation metrics or interpreting predictions
- Assess the impact of model complexity and the bias variance tradeoff on model performance
Summary: This section covers how to effectively select and implement models for a wide range of machine learning problems. You will learn to choose algorithms based on scenario requirements, handle imbalanced data, and leverage transformers and estimators within Spark ML workflows. Beyond algorithm selection, you will gain experience developing pipelines that prepare data, train models, and streamline reproducibility.
Hyperparameter tuning is a core focus here, with coverage of search methods including grid search, random search, Bayesian search, and the use of Hyperopt for advanced optimization. Additionally, you’ll build strong evaluation skills using classification and regression metrics, with emphasis on selecting the most relevant metric for a given project. Considerations around cross-validation, log-transform corrections, and tradeoffs between bias and variance further equip you to build high-performing, well-calibrated machine learning models.
Domain 4: Model Deployment (12% of the exam)
Model Deployment
- Identify the differences and advantages of model serving approaches: batch, realtime, and streaming
- Deploy a custom model to a model endpoint
- Use pandas to perform batch inference
- Identify how streaming inference is performed with Delta Live Tables
- Deploy and query a model for realtime inference
- Split data between endpoints for realtime inference
Summary: This section addresses how to take machine learning models from development into production with best practices for serving and inference. You will compare deployment approaches for batch, real-time, and streaming scenarios, learning to identify the right choice for the business and workload. Using pandas for batch inference and techniques for streaming with Delta Live Tables help prepare you for both large-scale offline scoring and continuous event-driven applications.
You will also practice deploying and querying custom endpoints for real-time inference, including splitting traffic between endpoints. These deployment skills are critical for ensuring that models provide value through timely predictions and reliability in operation. This domain prepares you to confidently transition from experimental results to models that are in active use by applications and teams.
Who Would Benefit Most From the Databricks Certified Machine Learning Associate Certification?
The Databricks Certified Machine Learning Associate certification is designed for anyone eager to demonstrate their ability to perform end-to-end machine learning tasks using the Databricks platform. It is an excellent fit if you are:
- A data scientist or analyst who wants to expand into machine learning with Databricks
- A machine learning engineer looking to validate hands-on skills in the Databricks ecosystem
- A student or early-career professional pursuing a path in data science and AI
- A technical professional transitioning into ML-focused projects and roles
By earning this credential, you show you can confidently use Databricks to build, tune, deploy, and manage models, setting a solid foundation for more advanced ML certifications and career growth.
What Job Roles Can This Databricks Certification Lead To?
Successfully passing this exam highlights your practical capabilities with Databricks Machine Learning, making you a strong candidate for roles like:
- Machine Learning Engineer (Associate-level)
- Data Scientist working with big data and Databricks pipelines
- AI/ML Analyst who applies models and insights across industries
- Data Engineer with ML specialization
- ML Operations (MLOps) Engineer contributing to pipelines and deployment
Additionally, this certification can give you a powerful edge if you're pursuing roles in organizations that rely on Databricks for large-scale machine learning workflows and real-time deployments.
How Long Do I Have to Complete the Databricks Certified Machine Learning Associate Exam?
From the start of the exam, you are given 90 minutes to complete all the questions. With thoughtful time management, most candidates find this enough to read, analyze, and answer each question carefully. Since the exam contains real-world scenarios and workflows, pacing yourself is important but very achievable.
How Many Questions Are on the Databricks Machine Learning Associate Certification Exam?
The exam consists of 48 scored questions. Each question is either multiple-choice or multiple-select, which means some questions may require selecting more than one correct answer. As with most certification exams, some unscored questions may appear for research purposes, but they are included seamlessly without affecting your performance or score.
What Is the Format of the Exam Questions?
The exam questions are presented in multiple-choice and multiple-select format. This means:
- Some items have only one correct answer
- Some items will have two or more correct answers
These formats are designed to assess your knowledge in realistic ML scenarios rather than rote memorization. You are tested on decisions you might face while working on Databricks ML projects, like choosing the right hyperparameter tuning strategy or identifying the proper deployment method.
What Is the Passing Score for the Databricks ML Associate Exam?
You must achieve a 70% passing score to earn the certification. The scoring model focuses on your total performance across all questions, so even if you are not perfect on each section, you can still successfully pass as long as your overall score meets the minimum threshold. This structure encourages thorough preparation while keeping the exam attainable for motivated learners.
How Much Does the Databricks Certified Machine Learning Associate Certification Cost?
Registering for the exam costs 200 USD. There may be additional local taxes depending on your country. Considering the value this adds in terms of skill recognition and career potential, many candidates view the fee as a worthwhile investment in their professional growth.
Is There a Validity Period for the Certification?
Yes. Once earned, the Databricks Certified Machine Learning Associate credential is valid for 2 years. After this period, you will need to recertify by completing the latest version of the exam. This ensures your skills stay current with evolving Databricks innovations and machine learning practices.
What Languages Is the Exam Offered In?
The exam is currently offered in English. Since the Databricks platform and most of its documentation are also English-based, this aligns well with preparing and practicing in realistic scenarios.
What Version of the Exam Should I Take?
The latest version of the exam is always considered the current "live" version. The Exam Code is the Latest Version and candidates should always prepare using the most recent exam guide published by Databricks. This ensures your study materials match the exact content and domains tested at your exam sitting.
Which Domains Will I Be Tested On, and What Are Their Weightings?
The exam is divided into four main domains, each representing critical areas of Databricks machine learning:
- Databricks Machine Learning (38%)
- MLOps, runtimes, AutoML, Unity Catalog feature store, MLflow usage, model versioning
- Data Processing / ML Workflows (19%)
- Data exploration, visualizations, transformations, handling missing values, encoding and scaling
- Model Development (31%)
- Algorithm selection, handling imbalance, pipelines, hyperparameter tuning, evaluation metrics
- Model Deployment (12%)
- Deploying models via batch, realtime, streaming, and serving endpoints
Your preparation should align with these percentages—heavier domains deserve more of your study hours.
Is There Any Required Work Experience Before Attempting This Exam?
There are no prerequisites for taking the Databricks Machine Learning Associate exam. However, Databricks highly recommends at least 6 months of practical, hands-on experience working on ML workflows in Databricks. This includes writing Python code, using Spark ML, feature stores, and MLflow. If you are new, combining training courses with sandbox practice will make you exam-ready.
How Hard Is the Databricks Machine Learning Associate Certification?
The certification is approachable and realistic for motivated professionals. The exam emphasizes real-world application of Databricks ML, not memorization of obscure details. As long as you take time for hands-on practice in Databricks notebooks and become familiar with AutoML, Spark ML, and Unity Catalog, you will find that the concepts flow naturally. Many candidates enjoy how well it mirrors the workflows they will actually encounter on the job.
What Study Resources and Training Should I Use?
The recommended resources include:
- Instructor-led training: Machine Learning With Databricks
- Self-paced training: Available in Databricks Academy with labs and exercises
- Hands-on practice: Using Databricks with demo data, AutoML experiments, and feature stores
- Exam Guide Review: The official Databricks Exam Guide covers objectives comprehensively
To top off your prep, practice with realistic Databricks Certified Machine Learning Associate practice exams that simulate the exam environment, making you confident and ready for the real test.
What Knowledge Areas Should I Focus Most On?
You should be comfortable in several key areas, such as:
- Databricks ML and AutoML workflows
- How AutoML assists in feature and model selection
- Benefits of using ML runtimes
- Feature Store and Unity Catalog
- Creating feature tables at account vs workspace level
- Online vs offline feature store use cases
- ML Workflows and Data Prep
- Handling missing values with mode, mean, median
- Appropriate times for one-hot encoding or log scaling
- Removing outliers with IQR or standard deviation
- Model Building and Evaluation
- Hyperparameter tuning with Hyperopt and fmin
- Cross-validation and train-validation split tradeoffs
- Understanding F1, RMSE, and ROC/AUC for evaluation
- Model Deployment
- Batch vs realtime vs streaming approaches
- Setting up model endpoints for inference
How Is the Databricks ML Associate Exam Delivered?
The exam is delivered online with remote proctoring. You can take it from your home or office as long as you have a stable internet connection, a camera-equipped computer, and a private, quiet testing space. The process makes scheduling convenient while upholding strict enforcement of exam integrity.
Are There Any Test Aides Allowed During the Exam?
No, this is a closed-book exam. You are not allowed any notes, documentation, or external tools during the test. All answers must come from your knowledge and preparation, which is why structured training and practice exams are invaluable.
How Do I Register for the Databricks Certified Machine Learning Associate Certification?
Registering is simple. You'll sign in to your Databricks Academy account, schedule your test with the chosen proctoring provider, and pay the 200 USD fee. Once scheduled, prepare thoroughly and bring your best effort—you’ll walk away with an industry-recognized credential.
Where Can I Find the Official Databricks Exam Page?
You can always find the latest details, including official training links and exam scheduling, on the Databricks Certified Machine Learning Associate official certification page. This is the best place to double-check the current exam guide and requirements before booking.
The Databricks Certified Machine Learning Associate certification is an excellent way to showcase your Machine Learning skills on an industry-leading platform. By preparing with the right resources, gaining hands-on exposure, and practicing with realistic exam simulations, you can step into the exam with confidence and leave certified—unlocking new opportunities in your data career.
