DB-ML
Databricks Machine Learning Associate
Certification Overview
Databricks's ML certification covering Feature Store, MLflow for experiment tracking, and model deployment on the Lakehouse platform. The 90-minute exam has 45 questions requiring 70% to pass. Recommends 6+ months hands-on experience. Valid 2 years. Online proctored exam.
The Databricks Machine Learning Associate (DB-ML) is a globally recognized benchmark designed for professionals aiming to prove their expertise in data. In today's competitive landscape, this certification acts as a critical signal to employers regarding your technical proficiency and commitment to the field.
Primary Impact
- Higher salary ceiling in Data roles
- Validated expertise at the enterprise level
Market Signal
Ranked as a Top Data Credential for 2026, holding the DB-ML significantly reduces the time-to-hire for senior positions.
Market Outlook
We monitor job market volume in real-time to provide the most accurate demand forecasting for your career.
Market Sentiment
There are currently 12 open roles in the US requiring this specific certification.
Tracking period: 12 Weeks
Job data provided by Adzuna
Maintenance & Recognition
Industry Recognition
Proctoring Options
Path to Excellence
Everything you need to successfully navigate the DB-ML certification journey.
01 Entry Requirements
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ML Engineering
Knowledge of Python, Spark, and basic machine learning workflows.
02 The Process
Feature Engineering
Learn to process and manage features using the Databricks Feature Store.
MLflow Mastery
Track, share, and deploy models using MLflow.
Pass ML Exam
Pass the Databricks Certified Machine Learning Associate exam.
Live Postings
Real-time Local Data8/7/2025
Senior Data Engineer
9/10/2025
Intermediate Strategic Data Specialist with Security Clearance
11/28/2025
IT Software Engineer
12/14/2025
Kinaxis Maestro Software Engineer
11/27/2025
Kinaxis Software Engineer
Job data powered by Adzuna
Ready to Get Certified?
Start your DB-ML certification journey today and open doors to new opportunities in data.