Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Generative AI Fundamentals and Concepts | 20-25% | - Vector embeddings and similarity search - Fine-tuning vs. retrieval approaches - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Prompt engineering principles |
| Architecture and Best Practices | 10-15% | - Cost management strategies - Performance optimization techniques - Security and privacy considerations - LLM pipeline architecture design - Monitoring and evaluation frameworks |
| Snowflake Cortex AI Capabilities | 25-30% | - Cortex AI functions and features - Model selection and cost optimization - Snowflake Copilot integration - COMPLETE function usage and parameters - Secure data handling in AI workflows |
| Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Unstructured data handling - Vector stores and embeddings in Snowflake - Document processing and chunking strategies |
| Cortex Analyst and Semantic Layer | 20-25% | - Business logic implementation in semantic models - Semantic model design and configuration - Performance tuning for analytical queries - Text-to-SQL translation and optimization |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?
A) Option A
B) Option E
C) Option D
D) Option C
E) Option B
2. An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?
A) Option A
B) Option E
C) Option D
D) Option C
E) Option B
3. An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?
A) 
B) 
C) 
D) 
E) 
4. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?
A) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
B) The
C) Using the
D) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
E) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
5. 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) 
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D,E | Question # 3 Answer: C | Question # 4 Answer: C,D | Question # 5 Answer: C |
1435 Customer Reviews
Quality and ValueITCertKing Practice Exams are written to the highest standards of technical accuracy, using only certified subject matter experts and published authors for development - no all study materials.
Tested and ApprovedWe are committed to the process of vendor and third party approvals. We believe professionals and executives alike deserve the confidence of quality coverage these authorizations provide.
Easy to PassIf you prepare for the exams using our ITCertKing testing engine, It is easy to succeed for all certifications in the first attempt. You don't have to deal with all dumps or any free torrent / rapidshare all stuff.
Try Before BuyITCertKing offers free demo of each product. You can check out the interface, question quality and usability of our practice exams before you decide to buy.
