Snowflake SnowPro Specialty - Gen AI Certification Exam Syllabus

GES-C02 Dumps Questions, GES-C02 PDF, SnowPro Specialty - Gen AI Exam Questions PDF, Snowflake GES-C02 Dumps Free, SnowPro Specialty - Gen AI Official Cert Guide PDF, Snowflake SnowPro Specialty - Gen AI Dumps, Snowflake SnowPro Specialty - Gen AI PDFThe Snowflake GES-C02 exam preparation guide is designed to provide candidates with necessary information about the SnowPro Specialty - Gen AI exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Snowflake Certified SnowPro Specialty - Gen AI exam.

It is recommended for all the candidates to refer the GES-C02 objectives and sample questions provided in this preparation guide. The Snowflake SnowPro Specialty - Gen AI certification is mainly targeted to the candidates who want to build their career in Specialty domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Snowflake SnowPro Specialty - Gen AI exam.

Snowflake GES-C02 Exam Summary:

Exam Name
 Snowflake SnowPro Specialty - Gen AI
Exam Code GES-C02
Exam Price $225 USD
Duration 85 minutes
Number of Questions 55
Passing Score 750 + Scaled Scoring from 0 - 1000
Recommended Training / Books Snowflake Gen AI Training
SnowPro Speciality: Gen AI Study Guide
Schedule Exam PEARSON VUE
Sample Questions Snowflake GES-C02 Sample Questions
Recommended Practice Snowflake Certified SnowPro Specialty - Gen AI Practice Test

Snowflake SnowPro Specialty - Gen AI Syllabus:

Section Objectives

Snowflake for Gen AI Overview - 18%
Define Snowflake’s Gen AI principles and features. - Snowflake Cortex
  • Cortex Models and Functions
  • Cortex Fine-tuning (Public Preview)
  • Cortex Search
    1. RAG use cases
    2. Unstructured data use cases
  • Cortex Analyst
    1. Text-to-SQL use cases
  • Cortex Agents

- Snowflake Cortex Code
- Cortex Code in Snowsight UI

  • Cortex Code Command Line (CLI)

- Snowflake Copilot Inline (Public Preview)

  • Cortex Models and Functions
  • Cortex Fine-tuning (Public Preview)
  • Cortex Search
    1. RAG use cases

- Snowflake Intelligence
- Different interfaces

  • AI Studio
  • SQL
  • REST API

- Bringing your own models into Snowflake

  • Snowflake Model Registry (custom model)
  • Snowpark Container Services
Outline Gen AI capabilities in Snowflake. - Prompting
- Cortex AI functions
  • Vector-embedding
  • Context Windows

- Cortex Search

  • Multi-index queries
  • Access control requirements
  • Different ways to use Cortex Search

- Cortex Analyst

  • Semantic Views
  • Semantic Views Autopilot
  • YAML Specification for Semantic Views
  • Verified Query
  • Custom Instructions

- Cortex Agents
- Snowflake Intelligence
- Cross-region inference

  • CORTEX_ENABLED _CROSS_REGION parameter
  • Considerations (e.g., latency, availability)

- REST APIs
- Model Context Protocol (MCP)
- Snowflake Cortex Code

  • Cortex Code CLI commands

- Cortex Knowledge Extensions (CKE)

Snowflake Gen AI Functions - 38%
Apply AI functions in Snowflake. - Snowflake Cortex AI functions
  • General
    1. AI_COMPLETE
    2. COMPLETE Structured Outputs
  • Task-specific functions
    1. AI_CLASSIFY
    2. AI_EXTRACT
    3. AI_PARSE_ DOCUMENT
    4. AI_SENTIMENT
    5. SUMMARIZE
    6. AI_SUMMARIZE_AGG
    7. AI_TRANSLATE
    8. AI_EMBED
    9. AI_FILTER
    10. AI_AGG
    11. AI_SIMILARITY
    12. AI_TRANSCRIBE
    13. AI_REDACT
  • Vector functions
    1. VECTOR_INNER _PRODUCT
    2. VECTOR_L1 _DISTANCE
    3. VECTOR_L2 _DISTANCE
    4. VECTOR_COSINE_SI MILARITY
    5. VECTOR_TRUNCATE
    6. VECTOR_NORMALIZE
    7. VECTOR_SUM
    8. VECTOR_MIN
    9. VECTOR_MAX
    10. VECTOR_AVG
  • Helper functions
    1. AI_COUNT_TOKENS
    2. TRY_COMPLETE
    3. SPLIT_TEXT_RECUR SIVE_CHARACTER
    4. SPLIT_TEXT_MARKD OWN_HEADER
    5. TO_FIE
    6. PROMPT
Perform data analysis given a use case. - Use fully-managed LLMs, RAG, and text-to-SQL services
  • Unstructured data
    1. Functions
    - AI_PARSE_ DOCUMENT
    - AI_EXTRACT
    - AI_SIMILARITY
    - AI_COMPLETE
  • Cortex Search
    1. Recursive split text markdown
    2. Chunk sizing
    3. Embedding models
    4. Semantic reranking
  • Multi-modal Analytics
    1. Audio and Image Processing

- Structured data

  • Functions
    1. AI_COMPLETE
  • Cortex Analyst
    1. Cortex Analyst Verified Query Repository (VQR)
    2. Integration with Cortex Search
    3. Suggested Questions
    4. CUSTOM_INST RUCTIONS

- Performance considerations

  • Choosing a model
    1. Latency (e.g., model size)
    2. Accuracy (e.g., fine-tuning, reducing hallucinations)
    3. Model capability
    4. Provisioned Throughput
Build or interact with interfaces to chat with data in Snowflake. - Set up the Snowflake environment
  • Required privileges

- Invoke Cortex functions within the application code (e.g., Streamlit in Snowflake)

  • Chat conversations
  • Multi-turn architecture
  • Update parameters (i.e., messages array for conversation history)

- Snowflake Intelligence
Apply Snowflake Cortex functions in data pipelines. - Snowflake Cortex
  • SQL interface
  • Data extraction
  • Data enrichment
  • Data augmentation
  • Data transformations
Run third-party models in Snowflake. - Using Snowpark Container Services
  • Environment setup
  • Docker images
  • Specification files
  • Create compute pool
  • Create image repository

- Using Snowflake Model Registry

  • Logging the model
  • Calling the model

Snowflake Gen AI Governance - 29%
Set up model access controls. - Limits on which models can be used
  • Restrict access to specific models
    1. Application roles
  • Control model access
    1. Role-Based Access Control (RBAC)
    2. Account-level allowlist parameter

- Data safety and security considerations

  • Cross region inference
  • Guardrails
  • Sensitive data management (e.g., AI_REDACT)
  • Methods to reduce model hallucinations and bias

- REST API authentication methods
Grant and revoke Role-Based Access Control (RBAC) and privileges. - Individual privileges
  • Specific requirements for Analyst, Search, Agents, and Snowflake Intelligence

- Roles

  • CORTEX_USER
  • CORTEX_ANALYST _USER
  • CORTEX_AGENT_USER
  • CORTEX_EMBED_USER
Manage, monitor, and optimize Snowflake Cortex costs. - Cortex Agents
  • Limit token usage

- Cortex Search

  • Different types of costs (e.g., virtual warehouse, EMBED_TEXT, serving, indexing)

- Cortex Analyst
- Cortex AI functions

  • Minimize tokens
  • Token cost implications

- Tracking costs of Snowpark Container Services

  • Compute pools

- Tracking model usage and consumption

  • Usage quotas
  • CORTEX_ANALYST _USAGE_HISTORY
  • CORTEX_AISQL_USAGE _HISTORY
  • CORTEX_SEARCH_DAILY _USAGE_HISTORY
  • CORTEX_REST_API_ USAGE_HISTORY
  • CORTEX_PROVISIONED _THROUGHPUT_USAGE_ HISTORY
  • METERING_DAILY_ HISTORY
  • METERING_HISTORY

- Object tagging to monitor AI services costs
Use Snowflake AI observability tools. - Snowflake AI observability features
  • Evaluation metrics
  • Comparisons
  • Tracing
  • Logging
  • Event tables

- Implementation methods

  • Trulens SDK

Snowflake Document Processing - 15%
Use document parsing functions. - AI_PARSE_DOCUMENT
  • OCR mode
  • LAYOUT mode
  • page_split
  • page_limit

- AI_EXTRACT

  • Response format
  • How to prompt/Prompt engineering
Prepare and manage documents and implement extracting workflows. - Upload documents
- Requirements (e.g., formats, size limits)
Build automated document processing pipelines with Cortex AI integration. - Orchestration of Snowflake tooling
  • Streams
  • Tasks
Troubleshoot and optimize document processing. - Extracting query errors
  • GET_PRESIGNED_URL function

- Requirements and privileges
- Cost and best practice considerations
- Fine-tuning arctic-extract models

 

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