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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Generative AI Fundamentals and Concepts	20-25%	- Vector embeddings and similarity search - Prompt engineering principles - LLM fundamentals and architectures - Fine-tuning vs. retrieval approaches - Retrieval-Augmented Generation (RAG) concepts
Topic 2: Snowflake Cortex AI Capabilities	25-30%	- COMPLETE function usage and parameters - Cortex AI functions and features - Snowflake Copilot integration - Secure data handling in AI workflows - Model selection and cost optimization
Topic 3: Architecture and Best Practices	10-15%	- Performance optimization techniques - LLM pipeline architecture design - Monitoring and evaluation frameworks - Security and privacy considerations - Cost management strategies
Topic 4: Data Preparation for Gen AI	15-20%	- Document processing and chunking strategies - Unstructured data handling - Vector stores and embeddings in Snowflake - Data governance for AI workloads
Topic 5: Cortex Analyst and Semantic Layer	20-25%	- Business logic implementation in semantic models - Performance tuning for analytical queries - Semantic model design and configuration - Text-to-SQL translation and optimization

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data analyst needs to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to streamline information retrieval from various contract documents. They are new to Cortex functions and want to understand access requirements and optimal usage. Which of the following statements about using SNOWFLAKE .CORTEX. EXTRACT_ANSWER are correct?

A) It is generally recommended to process multiple documents by applying EXTRACT_ANSWER to a table column containing the document texts, allowing for efficient batch processing.
B) EXTRACT_ANSWER is the most current and recommended function for all text extraction tasks, offering multi-label and image extraction capabilities.
C) For optimal accuracy, the source_document input should be in plain English, and the question should be specific, asking for a single value.
D) If EXTRACT_ANSWER encounters an unresolvable issue during processing, it returns NULL instead of an error, similar to TRY_COMPLETE.
E) The analyst's role must be granted the SNOWFLAKE. CORTEX_USER database role to execute EXTRACT_ANSWER functions.

2. A data governance team is concerned about the consistency and compliance of SQL queries generated by Cortex Analyst for sensitive financial reporting. They need to ensure that all generated SQL for a specific semantic model always includes a 'WHERE' clause that filters data for 'region = 'EMEA" and adheres to 'ISO 8601' date formatting for all date columns, regardless of the user's natural language input. Which of the following approaches is the MOST effective for implementing these strict, overarching requirements within Cortex Analyst's semantic model?

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

3. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) The

B) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
C) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

D) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
E) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.

A) Enabling Cortex Guard for 'AI_COMPLETE through the 'guardrails' option automatically bypasses the 'CORTEX_MODELS_ALLOWLIST to ensure all LLMs can be used for content safety filtering.
B) The parameter provides granular control at the database or schema level, allowing administrators to define different sets of approved models for different data contexts.
C) If a user attempts to call an LLM not explicitly listed in the via the Cortex LLM Playground, Snowflake will automatically fall back to the 'snowflake-arctic' model for that request.
D) The 'ACCOUNTADMIN' must execute the SQL command:

E) Users with 'ds_team_role' will still be able to successfully call

5. A Snowflake administrator is tasked with ensuring that a specific data science team can only use approved LLMs (mistral-7b, llama3.1-8b) for generative AI tasks within a particular schema, and also needs to enable the use of an LLM in a non-native region due to specific project requirements. Which combination of configurations would meet these requirements?