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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cortex Analyst and Semantic Layer | 20-25% | - Performance tuning for analytical queries - Business logic implementation in semantic models - Text-to-SQL translation and optimization - Semantic model design and configuration |
| Topic 2: Generative AI Fundamentals and Concepts | 20-25% | - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - Prompt engineering principles - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures |
| Topic 3: Data Preparation for Gen AI | 15-20% | - Document processing and chunking strategies - Vector stores and embeddings in Snowflake - Data governance for AI workloads - Unstructured data handling |
| Topic 4: Architecture and Best Practices | 10-15% | - Monitoring and evaluation frameworks - LLM pipeline architecture design - Performance optimization techniques - Security and privacy considerations - Cost management strategies |
| Topic 5: Snowflake Cortex AI Capabilities | 25-30% | - Snowflake Copilot integration - Model selection and cost optimization - Cortex AI functions and features - COMPLETE function usage and parameters - Secure data handling in AI workflows |
A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)
Correct Answer: A,E 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
A company is using Snowflake AI Observability to evaluate a summarization application. The application utilizes SNOWFLAKE. CORTEX. COMPLETE for LLM inference and a custom Python component for text pre-processing. The team is particularly interested in tracking detailed cost breakdowns and assessing the factual correctness of the LLM-generated summaries. Which of the following statements accurately describe the cost implications and evaluation metric capabilities in this scenario?
Correct Answer: A,B 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.
Correct Answer: A 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?
Correct Answer: A,B,C 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
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?
Correct Answer: A 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
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