Exam Name: SnowPro® Specialty: Gen AI Certification Exam
Certification Provider: Snowflake
Corresponding Certification: Snowflake Certification
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Updated: Sep 13, 2026
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From a free demo to downloadable study files and browser-based practice, Actual4dump keeps GES-C01 preparation in one place. Candidates preparing for Snowflake SnowPro® Specialty: Gen AI Certification in 2026 can review 351 practice questions, track progress, and keep materials current with free updates.
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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 |
| Passing Score: | 750/1000 |
| Exam Format: | Multiple select, Multiple choice |
| Exam Price: | $175 USD |
| Available Languages: | English |
| Real Exam Qty: | Approximately 65 |
| Related Certifications: | SnowPro Core Certification |
| Exam Duration: | 115 minutes |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Snowflake University Training Snowflake Documentation (Cortex & AI) |
| Exam Registration: | Snowflake Certification Portal Pearson VUE Snowflake Exams |
| Sample Questions: | DOWNLOAD DEMO |
| 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: Model Evaluation & Responsible AI | - Bias, fairness, and explainability considerations - Evaluation metrics for LLM outputs |
| Topic 2: Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Topic 3: Prompt Engineering | - Optimization of prompts for LLM outputs - Prompt design techniques |
| Topic 4: Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
| Topic 5: Embeddings, Vector Search & RAG | - Embeddings fundamentals - Vector search in Snowflake ecosystem - Retrieval-Augmented Generation (RAG) workflows |
| Topic 6: Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Topic 7: Use Cases & Solution Design | - End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake |
Snowflake SnowPro® Specialty: Gen AI Certification: Frequently Asked Questions
The GES-C01 exam leads to the SnowPro Specialty: Gen AI certification from Snowflake. It is positioned at the Specialty level. The exam validates the knowledge and skills expected of candidates preparing for Snowflake SnowPro® Specialty: Gen AI Certification.
Related certifications include:
- SnowPro Core Certification
The GES-C01 exam includes Approximately 65 questions and allows 115 minutes. Before test day, divide the available time by the number of questions in a timed practice set to establish a steady per-question pace. If a difficult item starts consuming too much time, mark it for review and move forward instead of losing momentum. A full timed practice session can help you decide when to move on, when to return, and how much buffer to reserve for a final check.
The passing score for GES-C01 is 750/1000, and the official exam fee is $175 USD. Because a retake requires paying the exam fee again, use timed practice results as a readiness check before scheduling your next attempt. If your performance remains inconsistent, continue reviewing weak domains and repeat a timed session before booking the exam.
No strict prerequisite required; recommended: SnowPro Core Certification or equivalent Snowflake data platform experience.
Eligibility rules can change, so confirm the current requirements on the official exam page before registering.
You can register through the official channels listed below:
The available exam delivery method is Online proctored exam via Pearson VUE and authorized test centers.
The official training options include:
After reviewing the recommended training, reinforce what you learned with Actual4dump's 351 practice questions for GES-C01.
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If you purchase the corresponding GES-C01 preparation material and do not pass that exam within 60 days of purchase, you may apply for a full refund under the 100% Money Back Guarantee. The request requires a scanned exam enrollment slip and the official Score Report PDF, submitted within two days after the exam. Eligible requests are processed within seven days.
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The current GES-C01 outline is organized into 7 top-level domains. The first listed areas include:
- Prompt Engineering
- Generative AI Fundamentals
- Data Governance & Security
Review the Exam Topics section above for the complete outline and any nested subtopics.
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A data application developer is building a Streamlit chat application that interacts with structured data using Cortex Analyst, a feature of Snowflake Cortex that is often integrated into Cortex Agents. The developer wants to ensure efficient multi-turn conversations and leverage verified queries. Which of the following statements represent best practices and functional aspects for this scenario within Snowflake's Gen AI ecosystem?
- A. Option B
- B. Option C
- C. Option A
- D. Option E
- E. Option D
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A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry. The goal is to deploy this model on Snowpark Container Services (SPCS) for scalable inference. The PyCaret model relies on the 'pycaret' and 'scipy' Python libraries, and the data scientist has local 'sample data.csv' for inferring the model's signature. Which statements are crucial for successfully logging this custom model for eventual SPCS deployment?
- A. Option B
- B. Option C
- C. Option A
- D. Option E
- E. Option D
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A data engineer is building a Snowflake data pipeline to ingest customer reviews from a raw staging table into a processed table. For each review, they need to determine the overall sentiment (positive, neutral, negative) and store this as a distinct column. The pipeline is implemented using SQL with streams and tasks to process new dat a. Which Snowflake Cortex LLM function, when integrated into the SQL task, is best suited for this sentiment classification and ensures a structured, single-label output for each review?
- A. Option B
- B. Option C
- C. Option A
- D. Option E
- E. Option D
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- A.
- B.
- C. Cross-region inference is automatically managed by Snowflake for allowed models, implying that a new, larger virtual warehouse is required to handle the cross- region data transfer overhead.
- D.
- E.
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An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?
- A. The
- B. CHANGE_TRACKING
- C. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
- D. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
- E. For embedding text, selecting a model like
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