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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Architecture and Best Practices	10-15%	- LLM pipeline architecture design - Performance optimization techniques - Cost management strategies - Monitoring and evaluation frameworks - Security and privacy considerations
Generative AI Fundamentals and Concepts	20-25%	- Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts - Prompt engineering principles - LLM fundamentals and architectures - Fine-tuning vs. retrieval approaches
Cortex Analyst and Semantic Layer	20-25%	- Semantic model design and configuration - Performance tuning for analytical queries - Business logic implementation in semantic models - Text-to-SQL translation and optimization
Snowflake Cortex AI Capabilities	25-30%	- COMPLETE function usage and parameters - Secure data handling in AI workflows - Cortex AI functions and features - Snowflake Copilot integration - Model selection and cost optimization
Data Preparation for Gen AI	15-20%	- Unstructured data handling - Data governance for AI workloads - Vector stores and embeddings in Snowflake - Document processing and chunking strategies

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

A) Option C
B) Option A
C) Option D
D) Option B
E) Option E

A) D If 'audit_status' is not found, the 'audit_status' key will be entirely absent from the JSON output for that document.
B) If a 'quantity' cell in is empty, the JSON output for that specific cell will include a 'score' key but omit the 'value' key.
C) The 'audit_statuS field, if not found, will appear in the JSON output as Taudit_status": [{"score": 0.0, "value": null}]}'.
D) The '_documentMetadata.ocrScore' field will always be present in the root of the JSON output, indicating the confidence in the optical character recognition process for the entire document.
E) For successfully extracted entities like 'net_profit' , the JSON output typically includes an array of objects, each containing both 'score' and 'value' keys.

3. An organization is implementing a two-tier LLM access control strategy in Snowflake. First, common models like 'mistral-7b' and 'llama3.1-8b' need to be broadly accessible to all users granted the 'SNOWFLAKE-CORTEX USER database role. Second, a specialized data science team, using the ANALYST ROLE', requires exclusive access to the higher-capability 'claude-3-5- sonnet' model, which should NOT be generally available through the broad access mechanism. Which set of SQL commands, executed by the 'ACCOUNTADMIN" role, correctly establishes this access control strategy?

A)

B)

C)

D)

E)

4. A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

A) Option C
B) Option A
C) Option D
D) Option B
E) Option E

5. A data engineering team aims to automatically classify incoming customer support requests into predefined categories ('Technical Issue', 'Billing Inquiry', 'General Question') as part of their Snowflake data ingestion pipeline. The goal is to achieve high classification accuracy while managing LLM inference costs efficiently. Which of the following strategies, when applied within a Snowflake data pipeline using Streams and Tasks, would best contribute to meeting these objectives?