As AI systems move from experimentation to real-world deployment, one truth is becoming clear: the quality of an AI system depends not just on the model, but on the context it receives. Context engineering—the discipline of designing, curating, and delivering the right information to AI systems at the right time—has quickly emerged as a critical capability in today’s AI landscape. Without it, even the most advanced models can produce incomplete, inaccurate, or inconsistent results. With it, organizations can build AI agents that are reliable, grounded in enterprise knowledge, and capable of handling complex, multi-step tasks.

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.

Finally, the certification addresses advanced scenarios such as multi-agent systems and long-horizon workflows, as well as techniques for evaluating context engineering decisions. Candidates are expected to measure how changes to context impact agent performance and use those insights to iterate and improve.

Professionals who earn this certification will be equipped to build and manage the information environment that powers AI agents on Databricks—ensuring those agents operate with the right context to deliver accurate, trustworthy outcomes.

If you’re interested in being among the first to take this exam, we invite you to join us at Data + AI Summit, where the beta version of the certification will be available for free. Don’t miss the opportunity to help shape the future of context engineering and validate your expertise at the forefront of AI innovation.

Notes: Each Data + AI Summit attendee can take the exam one time. The beta results will take 6-8 weeks to determine.