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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Best Practices | 10-15% | - Security and privacy considerations - LLM pipeline architecture design - Performance optimization techniques - Cost management strategies - Monitoring and evaluation frameworks |
| Topic 2: Data Preparation for Gen AI | 15-20% | - Vector stores and embeddings in Snowflake - Unstructured data handling - Data governance for AI workloads - Document processing and chunking strategies |
| Topic 3: Generative AI Fundamentals and Concepts | 20-25% | - LLM fundamentals and architectures - Fine-tuning vs. retrieval approaches - Prompt engineering principles - Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts |
| Topic 4: Snowflake Cortex AI Capabilities | 25-30% | - Model selection and cost optimization - COMPLETE function usage and parameters - Cortex AI functions and features - Snowflake Copilot integration - Secure data handling in AI workflows |
| Topic 5: Cortex Analyst and Semantic Layer | 20-25% | - Performance tuning for analytical queries - Text-to-SQL translation and optimization - Semantic model design and configuration - Business logic implementation in semantic models |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?
A)
B)
C)
D) 
2. A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.
A)
B)
C)
D)
E) 
3. An AI developer is testing a new RAG application in Snowflake. The application uses
in this scenario?
A) Option D
B) Option E
C) Option A
D) Option B
E) Option C
4. 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?
A) Option D
B) Option E
C) Option A
D) Option B
E) Option C
5. A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability with the TruLens SDK 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 D
B) Option E
C) Option A
D) Option B
E) Option C
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: C,D | Question # 4 Answer: C | Question # 5 Answer: B,C,D,E |








