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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake for Gen AI Overview | 26% | - Snowflake Copilot and AI assistant capabilities - Role-based access control (RBAC) for AI resources - Snowflake Gen AI principles and best practices - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs |
| Topic 2: Snowflake Gen AI Governance | 22% | - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation - Monitoring, logging, and observability - Cost management and token-based pricing - AI governance framework and policies |
| Topic 3: Snowflake Document AI | 12% | - Performance optimization and troubleshooting - Document AI setup and configuration - Data extraction and structured output - Document preparation and processing |
| Topic 4: Snowflake Gen AI & LLM Functions | 40% | - RAG implementation in Snowflake - API integration and authentication - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - Model deployment with Snowpark Container Services and Model Registry - Embedding functions: EMBED_TEXT_*, vector storage and similarity search |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?
A) The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.
B) When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.
C) Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.
D) Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.
E) To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
2. A Data Engineer is responsible for deploying machine learning models using Snowpark Container Services. They need to ensure that a specific role, model_deployer_role, has the appropriate permissions to create a Snowpark Container Service that uses an image from an existing image repository named my_inferenc_ images. Which of the following SQL commands grant the necessary privileges 'on the image repository' for this purpose?
A)
B)
C)
D)
E) 
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 D
B) Option A
C) Option C
D) Option B
E) Option E
4. A data engineer is designing an automated pipeline in Snowflake to process streaming customer support tickets using LLM functions. To ensure the pipeline is robust against unexpected LLM errors (e.g., due to malformed input or transient service issues), they decide to use SNOWFLAKE.CORTEX.TRY_COMPLETE instead of SNOWF LAKE .CORTEX.COMPLETE
. Which of the following is the most significant advantage of using
TRY_COMPLETE
in this scenario?
A) It returns
B) It automatically adjusts the LLM's
C) It offers enhanced security features, such as automatic input sanitization, which are not available in the standard
D) It processes tokens at a significantly reduced rate compared to
E) It provides a built-in retry mechanism for LLM calls that encounter temporary failures, automatically re-submitting the prompt up to three times.
5. 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) AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
B) Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
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) 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.
E) 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.
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: C,E | Question # 3 Answer: A,B,D,E | Question # 4 Answer: A | Question # 5 Answer: A,B,D |






