Generative AI Associate

Generative AI

Jul 1

Course Overview

Duration: 16 Hours
Format: Hands-on Course
Level: Intermediate

The Databricks Certified Generative AI Engineer Associate credential validates your ability to architect, implement, deploy, and govern production-grade Large Language Model (LLM) solutions. It proves you understand how to move beyond basic API wrapper scripts to build high-performance, enterprise-ready Generative AI systems.

This course bridges the gap between general AI theory and platform-specific execution on Databricks. Built directly from the official Exam Guide blueprint, this curriculum focuses intensely on the modern Mosaic AI ecosystem. You will dive deep into building Retrieval-Augmented Generation (RAG) applications, multi-stage agentic workflows, scalable vector indexing, and automated LLM evaluation.

Throughout this course, you will move beyond basic prompt engineering to master advanced production challenges: handling unstructured data chunking strategies, optimizing real-time Model Serving endpoints, debugging chain execution using MLflow Tracing, and implementing robust guardrails within Unity Catalog. By the time you graduate, you will possess both the technical confidence to pass the 45-question proctored exam and the hands-on engineering skills to deliver cutting-edge AI systems.

Book a Live, Virtual Session

Book a Live, In-Person Session

What You Will Learn

Domain 1: Design Applications

Craft precise system, user, and meta-prompts that elicit structured, deterministic outputs from LLMs.
Translate complex business requirements into multi-stage AI reasoning pipelines.
Choose the optimal foundation model strategy by evaluating critical trade-offs between proprietary external APIs, fine-tuned open-source models, and compact local models based on latency, cost, and accuracy constraints.

Domain 2: Data Preparation

Implement advanced semantic, structure-aware, and character-based document chunking strategies to match target context windows.
Filter out noisy, repetitive, or extraneous content from unstructured sources to optimize retrieval quality.
Select the correct Python library to extract and clean content from raw formats (PDFs, JSON, HTML, Markdown).
Evaluate embedding models to minimize semantic distortion and prevent silent token truncation errors.

Domain 3: Application Development — Heaviest Domain!

Orchestrate modular LLM chains and graph-based agents using developer frameworks like LangChain or LangGraph.
Build advanced Retrieval-Augmented Generation (RAG) systems that inject high-relevance domain context at runtime.
Create and bind deterministic execution tools (SQL execution, Web search, API callers) to AI agents to perform multi-stage problem decomposition.
Isolate and mitigate common LLM behavioral issues like hallucinations, prompt injection, and toxic outputs.

Domain 4: Assembling and Deploying Applications

Deploy scalable, production-ready REST APIs using Databricks Model Serving.
Spin up, configure, and manage Mosaic AI Vector Search indexes (comparing Standard vs. Storage-Optimized endpoints and Continuous vs. Triggered sync models).
Wrap multi-component pipelines into highly portable formats using the custom MLflow pyfunc flavor, mastering the critical separation between load_context() and predict().
Architect zero-downtime application upgrades using safe deployment strategies (Canary, Blue-Green).

Domain 5: Governance

Secure corporate intelligence by applying enterprise access control lists (ACLs) to model endpoints and vector indexes using Unity Catalog.
Apply programmatic data-masking and tokenization techniques to safeguard Personally Identifiable Information (PII).
Assess data lineage and model licensing requirements to eliminate legal, compliance, and copyright risks in corporate settings.

Domain 6: Evaluation and Monitoring

Instrument production pipelines with MLflow Tracing to record execution inputs, outputs, and intermediate latencies across every chain component.
Build automated evaluation routines using LLM-as-a-Judge frameworks to mathematically score metrics like Faithfulness, Context Precision, and Answer Relevancy.
Configure streaming Inference Tables to permanently log production payloads, detect statistical quality degradation, and isolate operational anomalies.

What’s Included in This Course

Hands-on Labs: Write clean Python and Databricks notebooks to build operational RAG systems, set up Vector Search syncs, deploy Model Serving endpoints, and evaluate traces.
Realistic Mock Exams: Comprehensive multiple-choice and multiple-selection practice tests matching the scenario-driven style, length, and technical depth of the official proctored test.
GenAI Architecture Guideheets: Downloadable summaries covering chunking heuristic triggers, vector configuration matrices, and MLflow logging templates.

Who This Course Is For

AI Engineers & Developers looking to acquire formal validation for their RAG, LLM application, and agent orchestration skills.
Data Engineers & Data Scientists wanting to bridge the gap between traditional data analytics/ML pipelines and generative AI engineering.
Solutions Architects tasked with designing secure, cost-controlled, and governed generative AI systems for enterprise organizations.

Course Requirements

Foundational proficiency in Python programming (working with APIs, lists, and basic dictionaries).
Conceptual understanding of machine learning fundamentals (embeddings, vectors, and tokenization concepts).
No advanced Spark or previous Databricks experience is necessary—the course covers all necessary ecosystem tooling from the ground up!