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| Section | Objectives |
|---|---|
| Prompt Engineering | - Optimization of prompts for LLM outputs - Prompt design techniques |
| Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Embeddings, Vector Search & RAG | - Vector search in Snowflake ecosystem - Retrieval-Augmented Generation (RAG) workflows - Embeddings fundamentals |
| Use Cases & Solution Design | - Enterprise AI application patterns in Snowflake - End-to-end GenAI solution architecture |
| Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
1. A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?
A) For tasks requiring LLMs to generate SQL queries from natural language, using the Cortex Analyst verified Query Repository (VQR) can improve accuracy by leveraging pre-verified SQL queries for similar questions.
B) AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
C) Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.
D) Deploying a custom fine-tuned model using SNOWFLAKE.CORTEX.FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
E) Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
2. A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?
A) The query
B) The query
C) The query
D) The query
E) The query
3. A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
4. 
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
5. A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large2' model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?
A) Option B
B) Option C
C) Option E
D) Option D
E) Option A
Solutions:
| Question # 1 Answer: A,B,E | Question # 2 Answer: C | Question # 3 Answer: A,C,D | Question # 4 Answer: A,D | Question # 5 Answer: B |
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