Databricks Practice Exams

Validates the ability to use Apache Spark DataFrame API and Spark SQL for data manipulation tasks, covering Spark architecture and execution model, DataFrame transformations and actions, Structured Streaming, Spark Connect, and performance tuning.

Validates the ability to perform data analysis tasks using Databricks SQL and the Data Intelligence Platform, covering data management with Unity Catalog, query development and optimization, dashboards and visualizations, AI/BI Genie spaces, and data modeling.

Validates the ability to perform data engineering tasks on the Databricks Lakehouse Platform, covering ELT with Spark SQL and PySpark, data pipeline development with Delta Lake and Databricks Workflows, data governance with Unity Catalog, and data quality management.

Validates advanced proficiency in building and optimizing production-grade data engineering solutions on Databricks, covering data processing with Delta Lake and Structured Streaming, data modeling using Medallion Architecture, Databricks tooling including Workflows and REST APIs, and security, governance, and deployment.

Validates the ability to design, develop, and deploy LLM-powered solutions on Databricks, covering RAG application design and data preparation, prompt engineering and retrieval chains, model serving and deployment, evaluation and monitoring for quality and safety, and governance with Unity Catalog.

Validates foundational knowledge of machine learning on the Databricks platform, covering AutoML, Feature Store, ML workflows and experiment tracking with MLflow, model development with Spark ML, and model deployment and serving.

Validates advanced expertise in designing and managing enterprise-scale machine learning solutions on Databricks, covering scalable model development with distributed training, MLOps practices including testing and deployment with Databricks Asset Bundles, and model monitoring with Lakehouse Monitoring.