Exam Prep – Databricks Certified Machine Learning Professional
Contact us to book this course
Learning Track
Exam Prep
Delivery methods
On-Site, Virtual
Duration
1 day
This course is a question-driven exam preparation program for the Databricks Certified Machine Learning Professional certification. Learners will engage with realistic practice questions covering all exam domains — model development, MLOps, and deployment — with instructors reviewing every answer option. Alongside explanations of why each choice is correct or incorrect, instructors will blend in targeted teaching on Databricks tools such as SparkML, MLflow, Feature Store, Lakehouse Monitoring, and Databricks Model Serving. Participants leave the course ready to succeed on the exam and confident in applying advanced machine learning engineering practices at enterprise scale.
Learning Objectives
By the end of this course, learners will be able to:
- Analyze and solve exam-style questions across all domains of the Databricks Certified Machine Learning Professional exam.
- Apply SparkML, distributed training, and advanced MLflow techniques to real-world ML pipelines.
- Build and evaluate feature pipelines with advanced Feature Store concepts, ensuring point-in-time correctness and real-time feature serving.
- Design, test, and monitor enterprise-scale ML workflows using Databricks MLOps capabilities.
- Implement drift detection and model health monitoring with Lakehouse Monitoring.
- Compare and implement model deployment strategies including blue-green, canary, and custom model serving.
Audience
- Candidates preparing for the Databricks Certified Machine Learning Professional exam
- Data scientists, ML engineers, and advanced data engineers with at least 1 year of Databricks experience
- Professionals seeking hands-on, exam-focused practice on advanced Databricks ML engineering concepts
Prerequisites
- Strong working knowledge of Python and ML libraries such as scikit-learn, SparkML, and MLflow
- Familiarity with Databricks ML environments, Unity Catalog, and asset management
- Experience building, training, and deploying ML models on the Databricks Lakehouse platform
Course outline
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
- Identifying when SparkML is the appropriate choice
- Building and tuning SparkML pipelines for batch, streaming, and real-time use cases
- Evaluating SparkML models and selecting inference methods
- Scaling distributed training with Spark, pandas Function APIs, and Ray
- Hyperparameter tuning with Optuna integrated with MLflow
- Comparing model parallelism vs. data parallelism strategies
- Advanced MLflow usage: Nested runs, logging custom metrics, creating custom model objects
- Advanced Feature Store: Point-in-time correctness, automated feature pipelines, online tables, and on-demand feature serving
- Model lifecycle management with Databricks features and workflows
- Implementing unit and integration testing in ML systems
- Designing scalable ML environments with Databricks Asset Bundles
- Automated retraining strategies triggered by drift or performance degradation
- Drift detection and Lakehouse Monitoring: building monitors, defining metrics, feature slicing, and alerting
- Tracking endpoint health via infrastructure metrics (latency, error rate, CPU/memory usage)
- Designing full monitoring pipelines for production ML
- Comparing deployment strategies (blue-green, canary) for enterprise workloads
- Implementing rollout strategies with Databricks Model Serving
- Registering and deploying custom PyFunc models in Unity Catalog
- Logging custom artifacts, querying models via REST API or MLflow Deployments SDK
- Serving custom models for batch and real-time inference with Databricks deployment tools
