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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cortex Analyst and Semantic Layer | 20-25% | - Performance tuning for analytical queries - Text-to-SQL translation and optimization - Business logic implementation in semantic models - Semantic model design and configuration |
| Topic 2: Snowflake Cortex AI Capabilities | 25-30% | - COMPLETE function usage and parameters - Snowflake Copilot integration - Model selection and cost optimization - Cortex AI functions and features - Secure data handling in AI workflows |
| Topic 3: Architecture and Best Practices | 10-15% | - Performance optimization techniques - LLM pipeline architecture design - Cost management strategies - Security and privacy considerations - Monitoring and evaluation frameworks |
| Topic 4: Generative AI Fundamentals and Concepts | 20-25% | - Prompt engineering principles - Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures - Fine-tuning vs. retrieval approaches |
| Topic 5: Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Vector stores and embeddings in Snowflake - Document processing and chunking strategies - Unstructured data handling |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
Question 1
A large e-commerce company plans to implement real-time sentiment analysis on millions of incoming customer reviews using SNOWFLAKE. CORTEX. SENTIMENT. They are concerned about managing costs and ensuring efficient processing. Which of the following statements about cost considerations and performance optimizations for SNOWFLAKE. CORTEX. SENTIMENT are true?
(Select all that apply)
A. Billing for SNOWFLAKE. CORTEX. SENTIMENT is primarily based on the number of output tokens generated in the response.
B. The actual number of tokens processed and billed for a SENTIMENT call is typically higher than the raw input text length, due to an internal prompt added by the function.
C. The newer AI_SENTIMENT function is a free, serverless alternative to SNOWFLAKE. CORTEX. SENTIMENT, offering cost savings for high-volume scenarios.
D. The fixed billing rate for the SENTIMENT function is 0.08 Credits per one million input tokens processed.
E. Snowflake recommends using a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for SENTIMENT function calls.
Question 2
A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?
A. AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
B. Deploying a custom fine-tuned model using SNOWFLAKE.CORTEX.FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
C. Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.
D. For tasks requiring LLMs to generate SQL queries from natural language, using the Cortex Analyst verified Query Repository (VQR) can improve accuracy by leveraging pre-verified SQL queries for similar questions.
E. Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
Question 3
A Data Application Developer is building a Streamlit chat application powered by Snowflake Cortex Analyst. Users frequently ask questions involving specific product names, such as "What was the total sales of 'Luxury Coffee Beans' last quarter?". The semantic model has a product_name dimension with high cardinality. The developer wants to ensure Cortex Analyst accurately identifies these specific product literals in user queries. Given this scenario, which of the following approaches should the developer consider to optimize literal search capabilities and enhance Cortex Analyst responses?
A. Option C
B. Option A
C. Option D
D. Option E
E. Option B
Question 4
A data pipeline processes thousands of scanned legal contracts daily using a Document AI model via the '!PREDICT function. The process involves fetching presigned URLs for documents from an internal stage using 'GET PRESIGNED URL'. Recently, the pipeline has started failing intermittently, returning the error:
The data engineering team confirms network connectivity and privileges are stable. Which of the following is the most likely cause of this issue?
A. The internal stage is not configured with
B. The
C. The documents contain handwritten annotations, which Document AI struggles to process effectively.
D. The virtual warehouse assigned to the Document AI pipeline is undersized (e.g., X-Small), leading to slow processing that indirectly causes URL expiration.
E. The Document AI model build has not been published to the production environment.
Question 5

Which of the following SQL snippets, when executed against a single invoice file like "invoice001 .pdf", correctly extracts and transforms the desired data, assuming 'json_content' holds the raw Document AI output?
A.
B.
C.
D.
E. 
Solutions:
| Question 1 Answer: B,D,E | Question 2 Answer: A,D,E | Question 3 Answer: E | Question 4 Answer: B | Question 5 Answer: C |


