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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
| Topic 2: Data Governance & Security | - Data privacy and access controls - Responsible use of AI in enterprise environments |
| Topic 3: Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Topic 4: Prompt Engineering | - Prompt design techniques - Optimization of prompts for LLM outputs |
| Topic 5: Use Cases & Solution Design | - End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake |
| Topic 6: Embeddings, Vector Search & RAG | - Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows - Vector search in Snowflake ecosystem |
| Topic 7: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
Question 1
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 A
B. Option D
C. Option B
D. Option C
E. Option E
Question 2
A retail company wants to implement an automated data pipeline in Snowflake to analyze daily customer reviews. The goal is to enrich a 'product_reviews_sentiment' table with sentiment categories (e.g., 'positive', 'neutral', 'negative') for each new review. They require the sentiment to be returned as a JSON object for downstream processing and need the pipeline to handle potential LLM errors gracefully without stopping. Assuming a stream 'new reviews_stream' monitors a 'customer _ reviews' table, which approach effectively uses a Snowflake Cortex function for this scenario?
A. Option A
B. Option D
C. Option B
D. Option C
E. Option E
Question 3
A data engineering team is building an automated pipeline in Snowflake to process incoming sensor dat a. Each sensor reading includes a 1024-dimensional feature vector, and the team needs to flag readings that are significantly different from a baseline reference vector using VECTOR_L1_DISTANCE
. The pipeline uses Snowflake tasks to orchestrate data loading and transformation. Which statement regarding the integration and operational aspects of this pipeline is true?
A. Snowflake Cortex AI SQL functions, including
B. To ensure efficient processing of new sensor data, the
C. The
D. The
E. For optimal performance when calculating
Question 4
A security-conscious data scientist in an Azure East US 2 (Virginia) account wants to fine-tune a mistral-7b model for a specific text summarization task and then deploy it for real-time inference using the Cortex REST API. The base model is natively mistral -7b available for fine-tuning in Azure East US 2 (Virginia). For subsequent inference using the fine-tuned model, they need to understand the regional and cross-region inference considerations. Which of the following statements are correct?
A. Option A
B. Option D
C. Option B
D. Option C
E. Option E
Question 5
An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?
A. Any error messages or warnings that occurred during the processing of the request.
B. The exact SQL queries that Cortex Analyst generated in response to user questions.
C. The complete request and response bodies associated with the application's execution steps.
D. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
E. The full text of the natural language questions submitted by the users.
Solutions:
| Question 1 Answer: A,B,C,E | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: A,B,C,E | Question 5 Answer: A,B,C,E |
