SnowPro Specialty: Gen AI (GES-C02) Course

This course contains the use of artificial intelligence.

Prepare for the SnowPro Specialty: Gen AI (GES-C02) exam and learn to build Gen AI solutions on Snowflake.

Generative AI on Snowflake isn't one feature. It's AI functions that run inside SQL, retrieval with Cortex Search, text-to-SQL with Cortex Analyst, agents that combine them, custom models in the Model Registry and Snowpark Container Services, and the governance that keeps all of it secure, accurate, and affordable. The SnowPro Specialty: Gen AI (GES-C02) exam tests every one of these, and it rarely asks for a definition. It describes a situation and offers two or three answers that all sound reasonable. To pass, you need to know which feature fits, which privilege is missing, which view shows the cost, and which function returns the right shape of output.

This course teaches exactly that, domain by domain, using real SQL and one realistic company you'll follow from start to finish.

Course at a glance

• 3 hours 31 minutes of video in 75 focused lectures, organized into 7 sections that follow the official exam domains

• An exam-style practice question at the end of every content lecture, with the reasoning behind each option

• 7 section quizzes to check your understanding before you move on

• A full-length, 55-question, 85-minute practice exam that matches the real exam's format, with an explanation for every option and a reference to the Snowflake documentation

• Captions on every lecture

One company, start to finish: Ridgeline Outdoor

Instead of disconnected snippets, every feature is introduced through a request from Ridgeline Outdoor, an outdoor gear retailer. Its customer experience team has 40,000 product reviews a quarter to classify and summarize. Its support team needs answers from 800 care and warranty articles. Its store managers want to ask sales questions in plain English. Its finance team receives supplier invoices as PDFs. And its platform admin has to decide who can use which models, keep personal data out of prompts, and explain the bill when AI spend jumps. You'll see Ridgeline's code, its mistakes, and its fixes, which is how the exam frames its scenario questions.

What makes this course different

• Mapped to the official domains and weights. Time is spent in proportion to the exam: Gen AI Functions (38%), Governance (29%), Overview (18%), and Document Processing (15%).

• Real code, checked against the documentation. Code slides show actual function signatures, grants, Cortex Search service definitions, semantic views, agent specifications, and pipeline SQL. The course also points out where widely shared study notes are out of date, for example newer AI_* function names, role-based model access, and current usage views.

• Worked scenarios in every domain. Step-by-step walkthroughs show how the pieces fit in practice: matching six business requests to Cortex features, triaging reviews in SQL, building a grounded support assistant, giving store managers an analyst, building an executive assistant with Cortex Agents, onboarding a new AI team safely, investigating a cost spike, evaluating an assistant before launch, fixing a stalled invoice pipeline, and building a contract-intake pipeline.

• Distinctions, not definitions. The final section collects the pairs the exam uses to separate right answers from distractors, such as Analyst versus Search, AI_EXTRACT versus AI_PARSE_DOCUMENT, Cortex Guard versus AI_REDACT, and daily versus hourly metering.

What you'll learn, section by section

Section 1: Welcome and exam orientation
The exam format, scoring, and domain weights, what each domain actually asks, a study plan, a low-cost hands-on setup using a trial account, and an introduction to Ridgeline Outdoor and its team.

Section 2: Domain 1, Snowflake for Gen AI overview
How Cortex AI fits together: LLM essentials such as tokens and context windows, the AI functions, Cortex Search, Cortex Analyst, Cortex Agents, and Snowflake Intelligence. When fine-tuning makes sense and when retrieval is better, how custom models run through the Model Registry and Snowpark Container Services, which interfaces exist (SQL, Python, REST, and the AI and ML Studio), and how to choose the right feature for a given need.

Section 3: Domain 2, AI functions in SQL
AI_COMPLETE with PROMPT, model options, structured outputs, and multimodal inputs. AI_CLASSIFY and AI_FILTER, AI_SENTIMENT with aspects, AI_SUMMARIZE and AI_TRANSLATE, the aggregate functions AI_AGG and AI_SUMMARIZE_AGG, embeddings and vector similarity, error handling with TRY functions, and how to design queries that are accurate and cost-aware. It ends with a worked review-triage pipeline.

Section 4: Domain 2, retrieval, analysis, chat, and pipelines
Retrieval-augmented generation with Cortex Search: chunking, service definitions, filters, and grounded prompts. Semantic views and Cortex Analyst, including verified queries and literal matching. Multi-turn chat and Streamlit in Snowflake, Cortex Agents and their tools, incremental AI pipelines with streams and tasks, and third-party models with Snowpark Container Services.

Section 5: Domain 3, Gen AI governance
The two layers of AI access (the USE AI FUNCTIONS privilege and the CORTEX_USER database role) and their defaults, role-based model access, cross-region inference, masking policies and AI_REDACT, Cortex Guard, cost drivers and the ACCOUNT_USAGE views that track them, and AI Observability with the RAG triad of context relevance, groundedness, and answer relevance.

Section 6: Domain 4, document processing
Stages, encryption, and directory tables. AI_PARSE_DOCUMENT in OCR and LAYOUT modes, with page splitting and page filters. AI_EXTRACT with questions and JSON schemas, including lists and tables. Automated pipelines with streams and tasks, a troubleshooting order for silent failures, and an end-to-end contract-intake build.

Section 7: Exam preparation and final review
Twenty distinctions the exam tests, rapid reviews of every domain, 24 new walkthrough questions with full reasoning, and exam-day strategy for timing and multiple-select questions.

How to get the most from this course

Watch the sections in order, because the Ridgeline case builds from lecture to lecture. When a practice question appears at the end of a lecture, pause and choose your answer before the explanation. Take each section quiz before moving on. If you have a Snowflake trial account, try the code on a few rows first; the course shows how to keep that inexpensive. Save the full practice exam for your final week, take it under timed conditions, and review every explanation, including the ones you answered correctly.

About the exam

GES-C02 has 55 questions, including multiple-choice and multiple-select items, with an 85-minute time limit. Scores are reported on a scale of 0 to 1000, and 750 is required to pass. There is no mandatory prerequisite certification, but Snowflake expects working SQL and data engineering knowledge, and many candidates also hold SnowPro Core. Always confirm current exam details with Snowflake before you register.

Who this course is for

• Data engineers, analytics engineers, and data scientists preparing for GES-C02

• Snowflake developers building RAG, text-to-SQL, chat, agent, or document-processing solutions with Cortex AI

• Platform administrators and governance leads who need to secure Gen AI features and control their cost

• SnowPro Core holders looking for a focused next certification

What this course is not

This is not an introduction to SQL or to Snowflake itself; you should already be comfortable with warehouses, roles, and stages. It doesn't teach Python model training in depth, and it focuses on what Snowflake provides for Gen AI rather than general machine learning theory.

Disclaimer: This course is an independent study resource. It is not affiliated with, endorsed by, or sponsored by Snowflake Inc. SnowPro® and Snowflake® are trademarks of Snowflake Inc. Exam details can change; always confirm them with Snowflake Inc. before registering.