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Snowflake GES-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Snowflake for Gen AI Overview	18%	- Model Development and Integration 1. Fine-tuning and selecting models for business requirements 2. Snowflake Model Registry and Snowpark Container Services - Generative AI Fundamentals and Snowflake Cortex 1. Cortex Search, Cortex Analyst, Cortex Agents, and Snowflake Intelligence 2. Snowflake Cortex AI architecture, features, and use cases 3. LLM concepts, capabilities, limitations, and model selection
Snowflake Gen AI and LLM Functions	38%	- Retrieval-Augmented Generation and AI Applications 1. Cortex Agents, REST APIs, and AI-powered application integration 2. Cortex Analyst, semantic views, and natural-language data queries 3. Cortex Search, chunking, embeddings, retrieval, and reranking - Cortex AI Functions 1. Text generation, classification, summarization, translation, and sentiment analysis 2. Embeddings, vector similarity, and multimodal AI processing - Production AI Workflows 1. Error handling, evaluation, and performance optimization 2. Integrating AI functions with streams, tasks, and dynamic tables
Snowflake Gen AI Governance	29%	- Access Control and Security 1. Model access controls, allowlists, and cross-region inference 2. Sensitive-data protection, Cortex Guard, and AI_REDACT 3. Cortex roles, privileges, and permissions for AI features - AI Quality and Observability 1. Evaluating AI outputs, groundedness, and relevance 2. Tracing, metrics, and production AI monitoring - Cost Management and Monitoring 1. Monitoring AI usage, consumption, and account-level costs 2. Budgets, warehouse sizing, and cost allocation
Snowflake Document Processing	15%	- Document Processing Pipelines 1. Stages, directory tables, and document ingestion 2. Automated processing with streams and tasks 3. Troubleshooting extraction accuracy, latency, and cost - Document Parsing and Extraction 1. AI_PARSE_DOCUMENT and OCR versus layout-based parsing 2. AI_EXTRACT and structured information extraction

Snowflake SnowPro Specialty: Gen AI Certification Sample Questions:

Question #1

A Gen AI Specialist is building a pipeline in Snowflake to gather data that will be used to summarize customer chat messages.
The following table, CHAT_MESSAGES, was created:
| CONVERSATION_ID | MESSAGE_TEXT |
| :--- | :--- |
| 1 | Hi, I need help with my order. |
| 2 | It's not been delivered yet. |
| 2 | How can I reset my password? |
| 3 | I received the wrong item. |
| 3 | Can I exchange it? |
The Specialist wants to use the Snowflake Cortex COMPLETE function with the llama3-8b model to generate a summary of each conversation. The prompt should begin with: "Summarize this chat:" followed by the full text of all messages in that chat conversation.
Which query will achieve this?
```sql

A. SELECT
conversation_id,
SNOWFLAKE.CORTEX.COMPLETE('llama3-8b', 'Summarize this chat:' || ARRAY_AGG (message_text)) AS summary_text FROM CHAT_MESSAGES;
B. SELECT
conversation_id,
SNOWFLAKE.CORTEX.COMPLETE('llama3-8b', 'Summarize this chat:' || LISTAGG(message_text)) AS summary_text FROM CHAT_MESSAGES GROUP BY conversation_id;
C. SELECT
conversation_id,
SNOWFLAKE.CORTEX.COMPLETE('llama3-8b', 'Summarize this chat:' || message_text) AS summary_text FROM CHAT_MESSAGES;
D. SELECT
conversation_id,
SNOWFLAKE.CORTEX.COMPLETE('llama3-8b', message_text) AS summary_text
FROM CHAT_MESSAGES
GROUP BY conversation_id;
```

Reveal Solution Discussion 0

Correct Answer: B 🗳️