Databricks Associate ML Engineer Certification

Module 1: ML Engineer Certification Overview

• Associate ML Engineer exam: format, domains, passing score, and registration process
• ML Engineer role in Indian tech companies: expectations and career pathways
• Exam domain breakdown: ML workflows, MLflow, deployment, and Databricks tools
• Self-assessment: 15-question diagnostic to identify knowledge starting point

Module 2: ML Workflows on Databricks

• End-to-end ML workflow: data -> features -> training -> evaluation -> deployment
• Databricks ML Runtime: pre-installed libraries and environment consistency
• Cluster configuration for ML: CPU vs GPU selection for exam scenarios
• Practice: 20 workflow and platform questions at associate difficulty

Module 3: Feature Engineering & Feature Store

• Feature Store API: creating feature tables and computing training datasets
• Point-in-time joins: avoiding label leakage in supervised learning problems
• Feature discovery: searching and reusing features across ML projects
• Practice: 15 Feature Store questions with detailed answer explanations

Module 4: MLflow Tracking & Experiments

• MLflow autolog: automatic parameter and metric logging for major ML libraries
• Custom logging: manually tracking business metrics alongside ML metrics
• Experiment comparison: finding the best run using the MLflow UI and API
• Practice: 20 experiment tracking questions covering API syntax and UI workflows

Module 5: MLflow Model Registry

• Model registration: logging models and transitioning through lifecycle stages
• Alias and tags: managing model metadata in the new MLflow Registry API
• Loading production models: mlflow.pyfunc.load_model() patterns for inference
• Practice: 20 Model Registry questions targeting exam-tested API calls

Module 6: AutoML & Model Deployment

• Databricks AutoML: configuration options, supported problem types, and output
• Model Serving: deploying endpoints and querying with REST API
• Batch inference: scoring DataFrames using logged MLflow models at scale
• Practice: 20 deployment and AutoML questions with solution explanations

Module 7: Full Mock Exam -- Attempt 1

• 45-question timed mock at Associate ML Engineer exam difficulty
• Answer review: detailed explanations covering correct answers and distractors
• Weak domain mapping: allocating revision time based on score by domain
• Lab exercises: hands-on MLflow and Feature Store tasks for reinforcement

Module 8: Full Mock Exam -- Attempt 2 & Final Readiness

• 45-question timed mock with a new question set for unpredictability practice
• Score trajectory analysis: confidence assessment for exam booking decision
• Syntax reference sheet: 30 critical API calls for last-day memorisation
• Certification guide: scheduling, rescheduling, and what to do after passing

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.