Master the Industry’s First Context Engineering Certification
Stop building fragile AI chats. Learn to design, secure, and scale low-latency, production-grade agentic workflows in this intensive, live Instructor-Led Training (ILT) program.
100% Mapped to the Official Databricks Certified Context Engineer Associate Exam Guide
The Reality Check: Why Simple RAG is Failing in the Enterprise
Simple prompt templates and basic vector search don't cut it anymore. As generative AI shifts from basic chat windows to autonomous, multi-agent systems, a brutal truth has emerged: The performance, cost, and accuracy of your AI agent is dictated entirely by the context it receives at inference time.
If your context window is bloated, your agents are slow, expensive, and prone to hallucinations. If it’s too restrictive, your agents fail to execute complex tasks.
To build enterprise-grade AI, you don’t just need a bigger model—you need precision context architecture.
Live Instructor-Led Training (ILT) This comprehensive, 16-hour live course is built from the ground up to match the official Exam Guide Blueprint. We go far beyond standard tutorials to cover the engineering rigor required for dependable, production-ready AI systems—delivered live with real-time instructor feedback and interactive labs.
Is This Course For You?
This intermediate-level ILT program is designed specifically for technical professionals who need to move past the "AI prototype" stage and want live, expert guidance:
AI Engineers & Developers: Move from basic chat wrappers to building robust, production-grade autonomous agents and multi-agent workflows.
Data & Analytics Engineers: Learn how to pivot your existing data governance, modeling, and pipeline expertise into crafting trusted, high-performance AI information environments.
Solutions Architects: Master the architectural patterns required to design secure, policy-compliant, and cost-optimized GenAI applications grounded cleanly in enterprise metadata.
What You Will Learn: The 7 Core Domains
Our curriculum maps perfectly to the practical and conceptual domains tested on the official proctored exam, broken down across 16 hours of live instruction.
Domain 1: Context Window Design & Prompt Engineering
Structure deterministic system prompts, meta-prompts, and boundary instructions.
Master advanced context window compression, compaction, and trimming strategies.
Minimize latency and token costs while ensuring the agent retains critical historical information.
Domain 2: Advanced Knowledge Retrieval & AI Search
Configure high-relevance ingestion and semantic chunking for RAG workflows.
Maximize retrieval accuracy using metadata filters, source authority, freshness boundaries, and advanced re-ranking.
Connect agents to live data spaces using Mosaic AI Vector Search and Genie spaces.
Domain 3: Memory Architectures & State Persistence
Understand the trade-offs between short-term ephemeral memory and persistent session memory.
Implement robust, serverless transaction state stores for AI agents using Databricks Lakebase (PostgreSQL engine).
Leverage MLflow to track, persist, and recall complex session histories across multiple model turns.
Domain 4: Model Context Protocol (MCP) & Tool-Use Design
Seamlessly link autonomous AI agents to enterprise internal tools and external environments using the Model Context Protocol (MCP).
Design tool selection criteria, enforce bounded/scoped tool access, and apply input/output verification to prevent execution vulnerabilities.
Domain 5: Governance, Quality, and Context Security
Enforce tight security boundaries using Unity Catalog data permissions, metadata tracking, and quality signals.
Secure compliance-heavy data feeds (e.g., healthcare, finance) via real-time PII masking, anonymization, and row/column-level access boundaries.
Domain 6: Multi-Agent Topologies & Long-Horizon Workflows
Architect multi-agent systems by dividing large enterprise problems into specialized, traceable sub-tasks.
Share relevant context between independent agents without duplicating information or overloading token bounds.
Build deep tracing mechanisms for multi-step execution flows using advanced AgentOps and MLflow Tracing.
Domain 7: Context Evaluation & Iteration
Establish rigorous frameworks to measure how context modifications impact factual grounding, accuracy, and overall agent performance.
Isolate context issues (hallucinations, excessive noise, or missing information) and systematically iterate on prompt/retrieval layouts.
What’s Included in Your Live Enrollment
When you join this ILT cohort, you aren’t just watching recorded videos. You are entering a live, collaborative engineering environment:
16 Hours of Live, Expert Instruction: Engage directly with your instructor, ask real-time questions, and walk through complex Databricks architecture as a group.
Live-Guided Lab Ecosystem: Build operational agent frameworks during the class with the instructor there to help troubleshoot and evaluate context issues.
Beta Blueprint Mock Exams: Gain access to scenario-based, multiple-choice practice tests meticulously styled after the official proctored Databricks certification questions.
Course Requirements
To get the most out of this live training, you should bring:
Python Proficiency: Functional experience working Python.
GenAI Fundamentals: A solid understanding of core LLM concepts, vectors, embeddings, and foundational RAG workflows.
Databricks Familiarity: Basic comfort navigating a Databricks workspace (Notebooks, Unity Catalog permissions).