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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Gen AI & LLM Functions | 40% | - API integration and authentication - Model deployment with Snowpark Container Services and Model Registry - RAG implementation in Snowflake - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE |
| Topic 2: Snowflake Gen AI Governance | 22% | - Audit and compliance for AI workloads - Cost management and token-based pricing - Guardrails, safety controls, and bias mitigation - AI governance framework and policies - Monitoring, logging, and observability |
| Topic 3: Snowflake Document AI | 12% | - Document preparation and processing - Performance optimization and troubleshooting - Document AI setup and configuration - Data extraction and structured output |
| Topic 4: 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 |
1. A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?
A) Option A
B) Option B
C) Option E
D) Option C
E) Option D
2. An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?
A) Option A
B) Option B
C) Option E
D) Option C
E) Option D
3. A data pipeline processes thousands of scanned legal contracts daily using a Document AI model via the '!PREDICT function. The process involves fetching presigned URLs for documents from an internal stage using 'GET PRESIGNED URL'. Recently, the pipeline has started failing intermittently, returning the error:
The data engineering team confirms network connectivity and privileges are stable. Which of the following is the most likely cause of this issue?
A) The Document AI model build has not been published to the production environment.
B) The virtual warehouse assigned to the Document AI pipeline is undersized (e.g., X-Small), leading to slow processing that indirectly causes URL expiration.
C) The documents contain handwritten annotations, which Document AI struggles to process effectively.
D) The
E) The internal stage is not configured with
4. 
Which of the following SQL snippets, when executed against a single invoice file like "invoice001 .pdf", correctly extracts and transforms the desired data, assuming 'json_content' holds the raw Document AI output?
A)
B)
C)
D)
E) 
5. A data scientist has developed a Hugging Face sentence transformer model for semantic search and needs to deploy it for GPU- powered inference using Snowpark Container Services (SPCS) in Snowflake. They've already trained the SentenceTransformer model locally. Which of the following statements correctly describe essential considerations for logging and deploying this model, ensuring it leverages GPU resources and appropriate dependencies?
A) Option A
B) Option B
C) Option E
D) Option C
E) Option D
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: B,D,E | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B,C,D |
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