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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Gen AI Governance | 22% | - AI governance framework and policies - Monitoring, logging, and observability - Cost management and token-based pricing - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation |
| Topic 2: Snowflake for Gen AI Overview | 26% | - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs - Snowflake Gen AI principles and best practices - Snowflake Copilot and AI assistant capabilities - Role-based access control (RBAC) for AI resources |
| Topic 3: Snowflake Document AI | 12% | - Document preparation and processing - Data extraction and structured output - Document AI setup and configuration - Performance optimization and troubleshooting |
| Topic 4: Snowflake Gen AI & LLM Functions | 40% | - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - RAG implementation in Snowflake - Model deployment with Snowpark Container Services and Model Registry - API integration and authentication - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
Question 1
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 D
B. Option A
C. Option B
D. Option C
E. Option E
Question 2
A development team is evaluating Snowpark Container Services (SPCS) for deploying various AI/ML workloads, including custom LLMs and GPU-accelerated model training. They need to understand its core benefits and operational characteristics compared to traditional container orchestration platforms. Which of the following statements accurately describe the benefits and/or operational characteristics of Snowpark Container Services for deploying third-party models and AI applications?
A. SPCS supports both long-running services (e.g., web applications) and finite-lifespan job services (e.g., GPU-accelerated machine learning model training).
B. SPCS provides a fully managed OCI runtime execution environment, allowing users to run containerized workloads directly within Snowflake without managing underlying Docker or Kubernetes infrastructure.
C. SPCS primarily supports applications written in Python and Java, with limited experimental support for other programming languages.
D. Snowpark Container Services ensures that data remains within Snowflake's security and governance boundaries, eliminating the need to move data out of the environment for processing.
E. Compute clusters within SPCS are designed to auto-scale dynamically based on workload demand, automatically adjusting the number of instances.
Question 3
An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?
A. Virtual warehouse compute used for refreshing the search service's index and processing base object changes.
B. Storage for the materialized source data and the optimized search index data structures within the Snowflake account.
C. ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
D. Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
E. Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.
Question 4
An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?
A. Option D
B. Option A
C. Option B
D. Option C
E. Option E
Question 5
A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?
A. Option D
B. Option A
C. Option B
D. Option C
E. Option E
Solutions:
| Question 1 Answer: A,B,C,E | Question 2 Answer: A,B,D | Question 3 Answer: A,B,C,D | Question 4 Answer: B,C | Question 5 Answer: D |







