Updated: Jul 21, 2026
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| Exam Name: | SnowPro Specialty: Gen AI Certification Exam (GES-C01) |
| Exam Number: | GES-C01 |
| Real Exam Qty: | Approximately 65 |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 115 minutes |
| Available Languages: | English |
| Exam Price: | $175 USD |
| Related Certifications: | SnowPro Core Certification |
| 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/ |
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Topic 2: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Topic 3: Use Cases & Solution Design | - End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake |
| Topic 4: Prompt Engineering | - Prompt design techniques - Optimization of prompts for LLM outputs |
| Topic 5: Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Topic 6: Embeddings, Vector Search & RAG | - Vector search in Snowflake ecosystem - Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows |
| Topic 7: Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
1. 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 complete request and response bodies associated with the application's execution steps.
C) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
D) The full text of the natural language questions submitted by the users.
E) Any error messages or warnings that occurred during the processing of the request.
2. A data engineering team is building a pipeline in Snowflake that uses a SQL task to call various Snowflake Cortex LLM functions (e.g., AI_COMPLETE, AI EMBED) on large datasets of customer interaction logs. The team observes fluctuating costs and occasional query failures, which sometimes halt the pipeline. To address these issues and ensure an efficient, robust, and monitorable pipeline, which of the following actions or considerations are essential? (Select all that apply.)
A) Option E
B) Option D
C) Option A
D) Option C
E) Option B
3. A data scientist is preparing a large text document for processing with Snowflake Cortex LLMs. They need to use the COUNT_TOKENS function to estimate the input token count for different models. Given the statement
which of the following statements about the COUNT_TOKENS function and its output is TRUE?
A) Option E
B) Option D
C) Option A
D) Option C
E) Option B
4. 

A) Option E
B) Option D
C) Option A
D) Option C
E) Option B
5. A data engineering team is building an automated pipeline within Snowflake to process newly ingested documents. This pipeline needs to classify each document's sentiment (positive, neutral, negative) and summarise its content using Cortex LLM functions, then store the results in a table. The pipeline is orchestrated using Streams and Tasks. Which considerations are paramount for implementing and monitoring this AI-infused data pipeline?
A) Option E
B) Option D
C) Option A
D) Option C
E) Option B
Solutions:
| Question # 1 Answer: A,B,D,E | Question # 2 Answer: A,C,E | Question # 3 Answer: B | Question # 4 Answer: B,E | Question # 5 Answer: C,D,E |
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