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SnowPro Specialty: Gen AI (GES-C02) Syllabus

Snowflake for Gen AI Overview

Snowflake's Gen AI principles, the Cortex AI suite and running and fine-tuning models in Snowflake.

18%
Weight
10
Questions
18
Marks

Cortex AI Suite and Best Practices

  • Snowflake Gen AI principles and best practices
  • Cortex AI features overview
  • Cortex Search and Cortex Analyst concepts
  • Choosing models and model families in Cortex

Custom and Open-Source Models

  • Snowpark Container Services for model hosting
  • Snowflake Model Registry
  • Cortex fine-tuning
  • Retrieval-augmented generation (RAG) patterns in Snowflake

Snowflake Gen AI Functions

Using Cortex AI SQL functions and LLM functions in queries and pipelines.

38%
Weight
21
Questions
38
Marks

LLM Functions

  • AI_COMPLETE (COMPLETE)
  • Task-specific functions (summarize, translate, sentiment, extract answer)
  • AI_CLASSIFY and AI_FILTER
  • AI_AGG and AI_SUMMARIZE_AGG
  • Prompt and model parameters

Embeddings and Search

  • Embedding functions (AI_EMBED / EMBED_TEXT)
  • Vector data type and vector similarity functions
  • Cortex Search services
  • Building RAG applications

Applied Use

  • Structured outputs
  • Using Cortex functions in Snowpark Python
  • Cortex REST API
  • Performance and token considerations

Snowflake Gen AI Governance

Controlling access, model availability, data location and cost for Gen AI workloads.

29%
Weight
16
Questions
29
Marks

Access and Model Control

  • SNOWFLAKE.CORTEX_USER database role
  • RBAC for Cortex features
  • Model allowlist (CORTEX_MODELS_ALLOWLIST)
  • Guardrails for LLM output

Data Location and Cost

  • Cross-region inference settings
  • Cortex credit consumption and token billing
  • Monitoring usage with account usage views
  • Cost controls and budgets

Snowflake Document Processing

Building and optimizing document parsing pipelines with Snowflake document processing functions.

15%
Weight
8
Questions
15
Marks

Parsing Documents

  • AI_PARSE_DOCUMENT (PARSE_DOCUMENT) OCR and layout modes
  • Staging files for processing
  • AI_EXTRACT for structured extraction
  • Supported file types and limits

Document Pipelines

  • Chunking parsed text (SPLIT_TEXT_RECURSIVE_CHARACTER)
  • Automating pipelines with streams and tasks
  • Optimizing document pipeline cost and performance
  • Feeding parsed documents into Cortex Search

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Learning Progress

Snowflake for Gen AI Overview 85%
Snowflake Gen AI Functions 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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