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Snowflake GES-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Governance & Security	- Responsible use of AI in enterprise environments - Data privacy and access controls
Topic 2: Generative AI Fundamentals	- Model capabilities and limitations - Core concepts of generative AI and LLMs
Topic 3: Prompt Engineering	- Prompt design techniques - Optimization of prompts for LLM outputs
Topic 4: Snowflake AI & Cortex	- Snowflake Cortex capabilities - AI functions and services in Snowflake
Topic 5: Embeddings, Vector Search & RAG	- Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows - Vector search in Snowflake ecosystem
Topic 6: Model Evaluation & Responsible AI	- Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations
Topic 7: Use Cases & Solution Design	- End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An organization relies on Snowflake Cortex LLM functions and has established a robust model governance policy using the 'CORTEX MODELS_ALLOWLIST parameter. A developer is integrating 'TRY COMPLETE into an application for processing various text inputs. Which of the following statements are correct regarding 'TRY COMPLETE and model access controls?

A) The parameter primarily governs access for models invoked by their plain names with but 'TRY COMPLETE can still access models if the user has been granted appropriate RBAC on a fully-qualified model object.
B) if a If a model specified in 'TRY_COMPLETE is not allowed by the and no RBAC grant exists for it, the 'TRY_COMPLETE function will automatically return 'NULL' to prevent a hard error.
C) The 'TRY_COMPLETE function can return a 'NULL ' value if the underlying LLM experiences a temporary internal processing error during response generation, allowing the query execution to continue without halting.
D) To provide a stateful conversational experience with 'TRY_COMPLETE, the function automatically retains context from previous calls within the same session.
E) If 'TRY_COMPLETE fails to perform its operation and returns ' NULL ' , the tokens that would have been processed for that specific call are not billed, ensuring cost efficiency for failed operations.

2. 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) The exact SQL queries that Cortex Analyst generated in response to user questions.
B) The full text of the natural language questions submitted by the users.
C) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
D) The complete request and response bodies associated with the application's execution steps.
E) Any error messages or warnings that occurred during the processing of the request.

3. A Gen AI Specialist is setting up Snowpark Container Services (SPCS) to host a custom open-source LLM. They need to understand the fundamental nature and constraints of image repositories within Snowflake. Which of the following statements accurately describe image repositories in Snowflake's Snowpark Container Services?

A) Image repositories are storage units within an image registry service, and they are used to store OCI-compliant container images.
B) Image repositories are primarily used for storing pre-trained Snowflake ML models and do not support custom third-party container images.
C) The maximum compressed layer size for an image registry is consistent across all cloud providers and regions, typically 160 GiB.
D) An image repository in Snowflake is an OCIv2 compliant service used exclusively for storing Docker images, not other OCI-compliant container images.
E) Dropping individual images from a repository is supported, allowing for granular management of stored container images.

4. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
B) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
C) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
D) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
E) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.

5. A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build ('invoice_extractor'). They then created an internal stage for documents. When they attempt to run the 'invoice_extractor!PREDlCT' method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?