Databricks · Professional
Databricks Machine Learning Professional
Operationalize machine learning at scale on Databricks: advanced MLflow, model lifecycle and deployment, MLOps automation, and Lakehouse Monitoring.
Machine Learning Professional exam at a glance
| Exam code | — |
|---|---|
| Vendor | Databricks |
| Level | Professional |
| Domains | 3 |
| Published practice questions | 58 |
| Our mock exam | 59 questions in 120 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | July 8, 2026 |
| Official exam page | Databricks — Machine Learning Professional |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official Machine Learning Professional exam page for the current format, price and scoring before you book.
Machine Learning Professional exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Model Development
44%Building scalable ML pipelines with Spark ML, distributed training and hyperparameter tuning, advanced MLflow tracking, and the Feature Store / feature pipelines.
- Scalable ML pipelines with Spark ML (Transformers, Estimators, Pipelines)
- Distributed training approaches on Databricks
- Hyperparameter tuning at scale (Hyperopt with SparkTrials)
- Advanced MLflow Tracking: autologging, nested runs, custom metrics/artifacts
- MLflow Models: flavors, signatures, input examples, custom pyfunc models
- Databricks Feature Store / Feature Engineering: feature tables and lookups
- Automated feature pipelines and point-in-time correctness
- Managing experiments and reproducibility
Free Model Development practice questions →Model Development study guide →
ML Ops
44%Operationalizing ML: the MLflow Model Registry, testing, CI/CD, automated retraining, environment management, and Lakehouse Monitoring for drift detection.
- MLflow Model Registry: registered models, versions, aliases/stages, transitions
- Model lifecycle governance and approval workflows (webhooks)
- Testing strategies for ML code and models (unit, integration)
- CI/CD for ML and environment management (Databricks Asset Bundles, Repos)
- Automated retraining workflows and Databricks Jobs orchestration
- Lakehouse Monitoring: data and model quality/drift detection
- Detecting data drift, concept drift, and triggering retraining
- Reproducibility, lineage, and cost/operational considerations
Model Deployment
12%Deployment paradigms on Databricks: batch, streaming, and real-time Model Serving, custom serving, and controlled rollout strategies.
- Deployment paradigms: batch scoring, streaming inference, real-time serving
- Databricks Model Serving endpoints for real-time inference
- Custom model serving (custom pyfunc, dependencies, init)
- Controlled rollout: A/B testing, canary, and traffic splitting between model versions
- Choosing a deployment approach by latency, throughput, and cost
- Serving models registered in the MLflow Model Registry
Free Model Deployment practice questions →Model Deployment study guide →
Machine Learning Professional FAQ
How is the Machine Learning Professional practice assessment configured?
Our practice format targets 59 questions in 120 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in Machine Learning Professional?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study Machine Learning Professional?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use Machine Learning Professional exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official Databricks exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.