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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Gen AI Governance | 22% | - 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 |
| Snowflake Document AI | 12% | - Performance optimization and troubleshooting - Document AI setup and configuration - Document preparation and processing - Data extraction and structured output |
| Snowflake for Gen AI Overview | 26% | - Snowflake Gen AI principles and best practices - Role-based access control (RBAC) for AI resources - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs - Snowflake Copilot and AI assistant capabilities |
| Snowflake Gen AI & LLM Functions | 40% | - Model deployment with Snowpark Container Services and Model Registry - RAG implementation in Snowflake - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - API integration and authentication - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE |
A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?
Correct Answer: D 🗳️
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A Gen AI Engineer is configuring a new semantic model for Cortex Analyst to process customer feedback. The goal is to ensure that when a user asks for sentiment analysis, the generated SQL queries always include an aggregation by a dimension and present the results as a percentage. The engineer plans to use custom instructions for this purpose. Which of the following details about is true and crucial for successful implementation?
Correct Answer: D 🗳️
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An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch', 'transformerS, 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPI, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPI. The engineer uses the Snowflake Model Registry to log the model. Which of the following 'log model' and 'create_service' configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?





Correct Answer: C 🗳️
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A data engineer is designing an automated data pipeline in Snowflake to process incoming customer feedback documents. The pipeline needs to perform the following steps: 1. Extract the overall sentiment from the feedback text. 2. Generate a concise summary of each feedback document. 3. Extract key entities (e.g., product, issue, customer name) into a structured JSON format using a powerful LLM, ensuring adherence to a predefined schema and graceful error handling. Which of the following Snowflake Cortex features and best practices should the data engineer leverage to build this robust AI-infused pipeline?
Correct Answer: B,D,E 🗳️
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An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1 ) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?
Correct Answer: A,C,D 🗳️
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