- Exam Code: GES-C01
- Exam Name: SnowPro® Specialty: Gen AI Certification Exam
- Certification Provider: Snowflake
- Corresponding Certification:Snowflake Certification
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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Document AI | 12% | - Document preparation and processing - Performance optimization and troubleshooting - Document AI setup and configuration - Data extraction and structured output |
| Topic 2: Snowflake for Gen AI Overview | 26% | - Role-based access control (RBAC) for AI resources - Snowflake Copilot and AI assistant capabilities - Snowflake Gen AI principles and best practices - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs |
| Topic 3: Snowflake Gen AI Governance | 22% | - Cost management and token-based pricing - Monitoring, logging, and observability - AI governance framework and policies - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation |
| Topic 4: Snowflake Gen AI & LLM Functions | 40% | - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - API integration and authentication - RAG implementation in Snowflake - Model deployment with Snowpark Container Services and Model Registry - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A security administrator needs to manage access to Snowflake Cortex LLM functions and models, and ensure compliant usage across different regions for various teams. Which configuration options and parameters are relevant for these requirements?
- A. Option A
- B. Option C
- C. Option B
- D. Option E
- E. Option D
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
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 A
- B. Option C
- C. Option B
- D. Option E
- E. Option D
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?
- A. Option A
- B. Option C
- C. Option B
- D. Option E
- E. Option D
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A data science team is using SNOWFLAKE. CORTEX. CLASSIFY_TEXT to categorize product reviews into detailed segments like 'Bug Report - Critical', 'Feature Request - UI/UX', 'General Praise', or 'Query - Billing Issue'. For highly nuanced reviews, they find the initial classifications lack precision, and they are also concerned about the associated compute costs for processing large volumes of dat a. Which strategies should they employ to optimize classification accuracy and manage costs effectively with this function?
- A. For complex scenarios where the relationship between review text and categories is not straightforward, including a concise task_description (e.g., 'Classify the product review focusing on technical support relevance') in the options argument is recommended to guide the model.
- B. To improve accuracy for ambiguous classifications, they should augment the list_of_categories with explicit description and examples for each category, understanding that these additions will increase input token costs for each record processed.
- C. If classifying thousands of reviews, they can significantly reduce overall compute costs by setting the temperature option to 0.0 within CLASSIFY_TEXT to ensure deterministic and cheaper inference.
- D. To reduce input token costs for classifications, the input text should be pre-processed to remove common stop words and punctuation, as these characters are counted as billable tokens without contributing to classification accuracy.
- E. CLASSIFY_TEXT labels, descriptions, and examples are counted as input tokens only once per function call, regardless of the number of records processed in a batch, to optimize cost efficiency.
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A business user frequently asks Cortex Analyst questions that require filtering on specific product names, such as "What were the sales for 'iced tea' last month?" The 'product' dimension has many distinct values (high cardinality), and Cortex Analyst sometimes struggles to accurately identify the exact literal product name, leading to less precise SQL queries. The Gen AI Specialist wants to enhance Cortex Analyst's ability to find these literal values for the 'product' dimension. To improve Cortex Analyst's literal search capability for the high-cardinality 'product' dimension, which of the following is the most appropriate and recommended approach to configure in the semantic model?
- A. Option A
- B. Option C
- C. Option B
- D. Option E
- E. Option D
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
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