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- Monitoring, logging, and observability
- Audit and compliance for AI workloads
- AI governance framework and policies
- Cost management and token-based pricing
- Guardrails, safety controls, and bias mitigation
Topic 2: Snowflake Document AI
12%
- Document AI setup and configuration
- Document preparation and processing
- Data extraction and structured output
- Performance optimization and troubleshooting
Topic 3: Snowflake for Gen AI Overview
26%
- Snowflake Gen AI principles and best practices
- Role-based access control (RBAC) for AI resources
- Snowflake Copilot and AI assistant capabilities
- Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs
Topic 4: Snowflake Gen AI & LLM Functions
40%
- Model deployment with Snowpark Container Services and Model Registry
- Embedding functions: EMBED_TEXT_*, vector storage and similarity search
- API integration and authentication
- Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE
- RAG implementation in Snowflake
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?
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An ML Engineer has developed a custom PyTorch model for image processing that requires GPU acceleration and specific PyPl packages ('torch' , 'torchvision'). They want to deploy it as a service on Snowpark Container Services (SPCS) using the Snowflake Model Registry. Which of the following statements are true regarding the deployment of this model to SPCS and its requirements? (Select all that apply.)
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A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?
A. For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.
B. Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.
C. Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.
D. The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
E. Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
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A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the ! PREDICT method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the ! PREDICT method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?
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A developer is building an interactive chat application in Snowflake leveraging the COMPLETE (SNOWFLAKE. CORTEX) LLM function to power multi-turn conversations. To ensure the LLM maintains conversational context and generates coherent responses based on prior interactions, which of the following methods correctly implements the passing of conversation history to the COMPLETE function?
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discussion
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