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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| 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 |
| Generative AI Fundamentals | - Core concepts of generative AI and LLMs - Model capabilities and limitations |
| Prompt Engineering | - Prompt design techniques - Optimization of prompts for LLM outputs |
| 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 |
| Use Cases & Solution Design | - Enterprise AI application patterns in Snowflake - End-to-end GenAI solution architecture |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?
A) Option E
B) Option D
C) Option C
D) Option A
E) Option B
2. A data engineering team is onboarding a new client whose workflow involves extracting critical financial data from thousands of daily scanned PDF receipts. They decide to use Snowflake Document AI and store all incoming PDFs in an internal stage name. After deploying their pipeline, they observe intermittent failures and varying error messages in the output, specifically:
Which two of the following actions are most likely required to resolve these processing errors?
A) Split any PDF documents exceeding 125 pages into smaller, compliant files, or reject them if splitting is not feasible.
B) Grant the 'SNOWFLAKCORTEX_USER database role to the role executing the '!PREDICT function.
C) Change the virtual warehouse size from an X-Small to a Large to improve Document AI processing speed.
D) Increase the 'max_tokens' parameter within the ' !PREDICT function options to accommodate longer document processing.
E) Ensure the internal stage is configured with 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')'.
3. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?
A) Option E
B) Option D
C) Option C
D) Option A
E) Option B
4. An administrator is reviewing their Snowflake bill and observes higher than expected storage and cloud services compute costs for a newly deployed Cortex Search Service. They need to investigate these charges. Which of the following statements correctly explains how these specific costs are incurred or can be monitored for a Cortex Search Service?
A) Storage costs are incurred for both the materialized source query data and the search index data structures, and these costs can be estimated by materializing the source query into a table using the CORTEX_SEARCH_DATA_SCAN table function, and then examining the size of that table.
B) Cloud Services compute costs for Cortex Search are always billed without any adjustments, regardless of the daily virtual warehouse compute costs, because they are considered serverless features.
C) The 'CORTEX_SEARCH_DAILY_USAGE_HISTORY view provides detailed breakdowns of storage costs per TB and cloud services compute credits incurred, including the 10% daily warehouse cost adjustment.
D) High cloud services compute costs for Cortex Search are primarily driven by the complexity of the embedding model selected and can be optimized by choosing a simpler model.
E) The 'CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the most appropriate tool to monitor Cortex Search storage and cloud services compute costs, as it tracks all ' Services usage.
5. A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?
A) Option E
B) Option D
C) Option C
D) Option A
E) Option B
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,E | Question # 3 Answer: A,B,D,E | Question # 4 Answer: A | Question # 5 Answer: D,E |
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