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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Cortex Analyst and Semantic Layer	20-25%	- Text-to-SQL translation and optimization - Performance tuning for analytical queries - Business logic implementation in semantic models - Semantic model design and configuration
Architecture and Best Practices	10-15%	- Monitoring and evaluation frameworks - LLM pipeline architecture design - Security and privacy considerations - Performance optimization techniques - Cost management strategies
Data Preparation for Gen AI	15-20%	- Vector stores and embeddings in Snowflake - Unstructured data handling - Data governance for AI workloads - Document processing and chunking strategies
Generative AI Fundamentals and Concepts	20-25%	- Fine-tuning vs. retrieval approaches - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures - Prompt engineering principles - Vector embeddings and similarity search
Snowflake Cortex AI Capabilities	25-30%	- Model selection and cost optimization - COMPLETE function usage and parameters - Secure data handling in AI workflows - Cortex AI functions and features - Snowflake Copilot integration

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question #1

A Snowflake administrator is tasked with monitoring the efficiency and cost-effectiveness of their Cortex Analyst deployments. They need to identify if certain semantic models are generating a high volume of failed or expensive queries. Which of the following approaches or statements are crucial for effectively monitoring and identifying issues with Cortex Analyst usage and associated costs?

A. Option D
B. Option E
C. Option A
D. Option B
E. Option C

Answer: B,C,E

Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).

Question #2

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 E
C. Option A
D. Option B
E. Option C

Answer: A,B,C,D

Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).

Question #3

data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?

A. When using
B. Storing vector embeddings, created using functions like
C. The
D.
E. Snowpark Python fully supports all Snowflake vector similarity functions, including

Answer: A

Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).

Question #4

A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

A. Option D
B. Option E
C. Option A
D. Option B
E. Option C

Answer: A,C,E

Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).

Question #5

An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment?