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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Embeddings, Vector Search & RAG | - Vector search in Snowflake ecosystem - Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows |
| Topic 2: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Topic 3: Model Evaluation & Responsible AI | - Bias, fairness, and explainability considerations - Evaluation metrics for LLM outputs |
| Topic 4: Generative AI Fundamentals | - Core concepts of generative AI and LLMs - Model capabilities and limitations |
| Topic 5: Use Cases & Solution Design | - Enterprise AI application patterns in Snowflake - End-to-end GenAI solution architecture |
| Topic 6: Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Topic 7: Prompt Engineering | - Optimization of prompts for LLM outputs - Prompt design techniques |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?
A) Option C
B) Option B
C) Option E
D) Option A
E) Option D
2. 
Which of the following is a 'direct cause' of this error related to missing schema-level privileges, assuming the model build name is unique and the warehouse is active and accessible?
A)
B)
C)
D)
E) 
3. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?
A) The
B) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
C) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like
D) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
E) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
4. 
database role. Which of the following 'privileges directly related to the virtual warehouse or task execution' are required for the process invoices_task' to successfully execute the Document AI '!PREDICT' method? (Select ALL that apply.)
A)
B)
C)
D)
E) 
5. A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?
A)
B)
C)
D)
E) 
Solutions:
| Question # 1 Answer: B,C,E | Question # 2 Answer: B | Question # 3 Answer: A,C,E | Question # 4 Answer: A,C,E | Question # 5 Answer: A,C,E |


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