Databricks certification practice questions & study guides

Build ELT pipelines and production workflows on the Databricks Lakehouse.

• Databricks Intelligence Platform6%
• Data Ingestion and Loading21%
• Data Transformation and Modeling22%
• Working with Lakeflow Jobs16%
• Implementing CI/CD10%
• Troubleshooting, Monitoring, and Optimization10%
• Governance and Security15%

Design, build, and deploy LLM-powered applications on Databricks.

• Design applications14%
• Data preparation14%
• Application development30%
• Assembling and deploying applications22%
• Governance8%
• Evaluation and monitoring12%

Use Databricks Machine Learning and MLflow to build, develop, and deploy machine learning models on the Lakehouse.

• Databricks Machine Learning38%
• ML Workflows19%
• Model Development31%
• Model Deployment12%

Operationalize machine learning at scale on Databricks: advanced MLflow, model lifecycle and deployment, MLOps automation, and Lakehouse Monitoring.

• Model Development44%
• ML Ops44%
• Model Deployment12%

Demonstrate advanced data engineering on Databricks: production code in Python and SQL, ingestion, transformation and quality, data sharing, monitoring, cost/performance optimization, security, governance, and deployment.

• Developing Code for Data Processing using Python and SQL22%
• Data Ingestion and Acquisition7%
• Data Transformation, Cleansing, and Quality10%
• Data Sharing and Federation5%
• Monitoring and Alerting10%
• Cost and Performance Optimization13%
• Ensuring Data Security and Compliance10%
• Data Governance7%
• Debugging and Deploying10%
• Data Modeling6%