DE MLO study guide
DE MLO · Work through your exam, one topic at a time.
The Databricks Certified Machine Learning Professional (often searched as DE MLO) is a vendor certification aimed at practitioners who build and operate machine learning workflows on the Databricks Lakehouse Platform. It suits data scientists, ML engineers, and MLOps engineers who already work with MLflow, Spark, and Delta Lake and want independent evidence that they can take a model from experimentation through deployment and ongoing monitoring. The DE MLO study guide has 5 sections and about 2,400 words.
01Experiment Tracking and Model Lifecycle
This domain covers MLflow Tracking and the path from an experiment run to a registered model version and deployment alias. Questions test whether you know what belongs in a run, how runs relate to experiments and registered models, and how Unity Catalog changes model naming, permissions, and lifecycle promotion.
465 words, about 2 min02Model Development, Training, and Tuning
This domain tests how you build reproducible training workflows on Databricks: Spark ML pipelines, hyperparameter tuning, and Feature Store integration. Expect questions on avoiding leakage, choosing validation strategies, and logging the tuning process so the best model can be reproduced.
470 words, about 2 min03Model Deployment and Serving
This domain covers moving a model from the registry into batch, streaming, or real-time serving. It tests endpoint configuration, traffic management, inference logging, permissions, and the failure modes that appear when a model is served without a complete schema or stable version pointer.
467 words, about 2 min04Model Monitoring and Maintenance
This domain tests how you detect degradation after deployment and decide when to retrain. Expect questions on Lakehouse Monitoring, inference tables, drift metrics, ground-truth joins, alerting, and the difference between data drift, concept drift, and model quality loss.
496 words, about 2 min05ML Governance and Reproducibility
This domain tests whether you can govern models and reproduce results later. Questions cover Unity Catalog permissions and lineage, model ownership, audit logs, immutable versions, Git integration, environment capture, and the evidence needed to explain how a deployed model was produced.
502 words, about 3 min