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Snowflake GES-C01 Exam Syllabus Topics:

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

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

A) Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
B) Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of 'NULL' values or empty cells where appropriate.
C) Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
D) Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
E) Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.

2. 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 B
B) Option A
C) Option E
D) Option D
E) Option C

3. A data engineer is developing a Snowflake Cortex LLM application that processes sensitive customer feedback. To ensure that generated responses from the 'COMPLETE' function are filtered for potentially unsafe or harmful content, they need to enable Cortex Guard. Which of the following SQL 'COMPLETE function calls correctly demonstrates the enablement of Cortex Guard with the default unsafe response message?

A)

B)

C)

D)

E)

4. A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

A)

B)

C)

D)

E)

5. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?