At the same time, demand for skilled practitioners in this space is growing rapidly. Databricks certifications have long been recognized across the industry as a benchmark for expertise in data and AI, helping professionals validate their skills and organizations identify top talent. Building on that momentum — and following the strong industry adoption of certifications like the Databricks Certified Generative AI Engineer Associate — Databricks continues to lead from the front in defining and validating the next generation of AI skills. Today, we’re excited to introduce the Databricks Certified Context Engineer Associate: the industry’s first certification purpose-built for this emerging field, setting a new standard for practitioners developing context-aware AI systems.

This certification is designed to assess an individual’s ability to design, assemble, and govern the information that AI agent systems receive at inference time using Databricks. It reflects the real-world challenges teams face when deploying AI systems that must reason over enterprise data, tools, and workflows.

The exam covers several key areas of context engineering. Candidates are evaluated on their ability to structure effective system prompts and instructions, ensuring agents behave predictably and align with intended goals. They must also demonstrate how to configure retrieval systems—such as Vector Search—to surface the most relevant knowledge at inference time.

Beyond retrieval, the certification explores how to design memory architectures that allow agents to persist and reuse state across sessions. Using tools like Lakebase and MLflow, candidates show how to build systems that maintain continuity and improve over time. The exam also tests the ability to integrate agents with external tools and data sources using protocols such as MCP, enabling agents to take meaningful actions in real-world environments.

Another critical component is managing context window constraints. Candidates must understand how to apply compaction and trimming strategies so that agents can operate efficiently without losing essential information. Just as importantly, the exam emphasizes governance—ensuring that only high-quality, policy-compliant data enters the context. This includes leveraging Unity Catalog for metadata management, enforcing data quality standards, handling PII appropriately, and applying access controls.