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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Gen AI Governance | 22% | - Guardrails, safety controls, and bias mitigation - AI governance framework and policies - Monitoring, logging, and observability - Cost management and token-based pricing - Audit and compliance for AI workloads |
| Topic 2: Snowflake Document AI | 12% | - Performance optimization and troubleshooting - Data extraction and structured output - Document AI setup and configuration - Document preparation and processing |
| Topic 3: Snowflake for Gen AI Overview | 26% | - Snowflake Copilot and AI assistant capabilities - Snowflake Gen AI principles and best practices - Role-based access control (RBAC) for AI resources - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs |
| Topic 4: Snowflake Gen AI & LLM Functions | 40% | - RAG implementation in Snowflake - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - Model deployment with Snowpark Container Services and Model Registry - API integration and authentication - Embedding functions: EMBED_TEXT_*, vector storage and similarity search |
A data engineer is integrating a custom application with Snowflake Cortex to leverage the 'COMPLETE' function via its REST API. They are preparing a 'curl' request to send a prompt to the 'mistral-large? model. Which of the following 'curl' command configurations correctly specifies the ''mandatory'' authentication header and a valid token type for accessing the Cortex REST API?
Correct Answer: C 🗳️
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A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml' file for a long- running service that uses this image, custom environment variables, and requires external access?
Correct Answer: A,B,E 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?
Correct Answer: B 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
A financial institution is fine-tuning a llama3.1-70b model within Snowflake Cortex using sensitive internal financial reports to improve sentiment analysis on earnings call transcripts. They need to understand the implications for data privacy, model ownership, and how this fine-tuned model can be managed and shared. Which of the following statements are true regarding this process?
Correct Answer: B,C,D 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
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