Snowflake GES-C01 Questions & Answers - in .pdf
- Total Q&A: 351
- Update: Sep 10, 2026
- Price: $59.99
- Vendor: Snowflake
- Exam Code: GES-C01
- Exam Name: SnowPro® Specialty: Gen AI Certification Exam
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Snowflake GES-C01 Q&A - Testing Engine
- Total Q&A: 351
- Update: Sep 10, 2026
- Price: $59.99
- Vendor: Snowflake
- Exam Code: GES-C01
- Exam Name: SnowPro® Specialty: Gen AI Certification Exam
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Snowflake GES-C01 Exam Overview:
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Specialty: Gen AI Certification Exam (GES-C01) |
| Exam Number: | GES-C01 |
| Exam Format: | Multiple select, Multiple choice |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Exam Price: | $175 USD |
| Real Exam Qty: | Approximately 65 |
| Related Certifications: | SnowPro Core Certification |
| Available Languages: | English |
| Passing Score: | 750/1000 |
| Recommended Training: | Snowflake University Training Snowflake Documentation (Cortex & AI) |
| Exam Registration: | Pearson VUE Snowflake Exams Snowflake Certification Portal |
| Sample Questions: | Snowflake GES-C01 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE and authorized test centers |
| Pre Condition: | No strict prerequisite required; recommended: SnowPro Core Certification or equivalent Snowflake data platform experience. |
| Official Syllabus URL: | https://www.snowflake.com/training-and-certification/ |
Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Prompt Engineering | - Prompt design techniques - Optimization of prompts for LLM outputs |
| Topic 2: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Topic 3: Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Topic 4: Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Topic 5: Generative AI Fundamentals | - Core concepts of generative AI and LLMs - Model capabilities and limitations |
| Topic 6: Embeddings, Vector Search & RAG | - Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows - Vector search in Snowflake ecosystem |
| 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:
Question #1
A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?
A. Option A
B. Option E
C. Option D
D. Option C
E. Option B
Question #2
A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?
A. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
B. Using the
C. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
D. The
E. The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
Question #3
After resolving this, they try to process a batch of 1500 documents in a single query using the method, which also fails. Which two issues are most likely contributing to these failures?
A. The batch of documents exceeds Document AI's limit of 1000 documents per query.
B. The 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role or the 'CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE' privilege on the schema has not been granted to the .
C. The virtual warehouse being used for Document AI is not a Snowpark-optimized warehouse.
D. The name is not unique within the .
E. The has not been granted the 'CREATE STREAM' privilege on the
Question #4
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?
A. Option A
B. Option E
C. Option D
D. Option C
E. Option B
Question #5
A data engineer is integrating a custom application with Snowflake Cortex to leverage the 'COMPLETE' function via its REST API. They are preparing a 'curl' request to send a prompt to the 'mistral-large? model. Which of the following 'curl' command configurations correctly specifies the ''mandatory'' authentication header and a valid token type for accessing the Cortex REST API?
A. 
B. 
C. 
D. 
E. 
Solutions:
| Question #1 Answer: B,D,E | Question #2 Answer: B,C | Question #3 Answer: A,B | Question #4 Answer: A,C,D | Question #5 Answer: D |
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