Exam Name: SnowPro® Specialty: Gen AI Certification Exam
Certification Provider: Snowflake
Corresponding Certification: Snowflake Certification
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Updated: Oct 14, 2026
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- Total Questions: 351
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Snowflake GES-C01 Exam Overview:
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro® Specialty: Gen AI Certification Exam |
| Exam Number: | GES-C01 |
| Real Exam Qty: | 100 |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice, Multiple Select |
| Certificate Validity Period: | 2 years |
| Related Certifications: | SnowPro Core Certification SnowPro Architect SnowPro Data Engineer |
| Exam Price: | USD 200 |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam (ONDEMAND) |
| Pre Condition: | Recommended: SnowPro Core Certification or equivalent hands-on experience with Snowflake platform |
| Official Syllabus URL: | https://docs.snowflake.com/en/certification/snowpro/specialty/gen-ai |
Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for Gen AI | 15-20% | - Vector stores and embeddings in Snowflake - Data governance for AI workloads - Document processing and chunking strategies - Unstructured data handling |
| Cortex Analyst and Semantic Layer | 20-25% | - Business logic implementation in semantic models - Text-to-SQL translation and optimization - Performance tuning for analytical queries - Semantic model design and configuration |
| Snowflake Cortex AI Capabilities | 25-30% | - COMPLETE function usage and parameters - Snowflake Copilot integration - Secure data handling in AI workflows - Cortex AI functions and features - Model selection and cost optimization |
| Architecture and Best Practices | 10-15% | - Security and privacy considerations - Cost management strategies - LLM pipeline architecture design - Performance optimization techniques - Monitoring and evaluation frameworks |
| Generative AI Fundamentals and Concepts | 20-25% | - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Prompt engineering principles - Vector embeddings and similarity search - Fine-tuning vs. retrieval approaches |
GES-C01 Exam FAQ: What Candidates Ask Most
The Snowflake GES-C01 exam, officially titled SnowPro® Specialty: Gen AI Certification Exam, is the required test for earning the Snowflake Certification certification, a credential at the Specialty level. Passing it validates the skills Snowflake expects from certified professionals, and it can also support progress toward related credentials such as SnowPro Core Certification, SnowPro Architect, SnowPro Data Engineer.
The GES-C01 exam includes 100 questions, and you have 90 minutes to complete it. Before exam day, divide the available time by the question count so you know the pace you need to hold, and practice flagging time-consuming items for review instead of stalling on a single question. Timed sessions in the ActualTestsIT test engine are the easiest way to build that rhythm before it counts.
You need 70% to pass the GES-C01 exam, and the official registration fee is USD 200. Keep in mind that a failed attempt means paying that fee in full again for a retake, so avoid booking your seat on a hunch. Work through the ActualTestsIT practice test until your scores sit comfortably above the passing requirement before you schedule the exam.
Snowflake asks candidates to meet the following requirement before registering: Recommended: SnowPro Core Certification or equivalent hands-on experience with Snowflake platform. Exam policies do change, so confirm the latest details on the official exam page at https://docs.snowflake.com/en/certification/snowpro/specialty/gen-ai before you book.
Yes. ActualTestsIT offers a free PDF demo of the Snowflake SnowPro® Specialty: Gen AI Certification practice questions, so you can judge the quality and format before purchasing. After you buy, your purchase includes 365 days of free updates; if the product expires after that period, you can extend the update service at a 50% discount from your member zone.
Every ActualTestsIT order is covered by a 100% Money Back Guarantee. If you take the corresponding GES-C01 exam within 60 days of purchase and do not pass, send a scan of your exam enrollment slip together with your official Score Report PDF within two days of the exam date, and your claim will be processed within seven days. The candidate name must match the payer name, and the guarantee does not apply if you take the exam within three days of purchase, if you downloaded the product but never took the exam, or to free materials and expired orders. If you would rather have fresh material than a refund, you can exchange your purchase for two additional exam products of equal value at no cost and keep the update service on your original product. Delivery itself is instant: your download is available right after payment and a copy is emailed to you within one minute — if nothing arrives within two hours, contact our support team. You may install the software on as many computers as you need.
The Snowflake SnowPro® Specialty: Gen AI Certification exam is organized into 5 major domains. Some of the key domains include:
- Cortex Analyst and Semantic Layer, accounting for 20-25% of the exam
- Generative AI Fundamentals and Concepts, accounting for 20-25% of the exam
- Architecture and Best Practices, accounting for 10-15% of the exam
Scroll up to the Exam Topics section for the complete breakdown, and use it to plan how much study time each domain deserves.
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)
- A. Option B
- B. Option C
- C. Option A
- D. Option D
- E. Option E
Correct Answer: A,B 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A team is planning the implementation of a new Document AI solution and needs to be aware of the specific guidelines and limitations concerning naming conventions and task management within Snowflake. A primary concern is to avoid common pitfalls that could lead to errors or unsupported configurations.
- A. Database and schema identifiers referenced in Document AI operations must not include double quotes, as this is an unsupported syntax.
- B. For optimal cost management and to avoid resource contention, Document AI tasks should leverage serverless task configurations.
- C. Document AI supports concurrent user activity on the same model build in Snowsight, enabling multiple team members to upload documents and review answers simultaneously.
- D. Document AI model builds can be renamed after their initial creation and publication to align with evolving project naming standards.
- E. While optional, creating a separate database and schema specifically for Document AI assets is a best practice for organization and cost visibility.
Correct Answer: A,E 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?
- A. Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
- B. Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
- C. Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
- D. Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
- E. Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
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_extractofl). They then created an internal stage for documents. When they attempt to run the 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?
- A.
- B.
- C.
- D.
- E.
Correct Answer: E 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A machine learning team is leveraging the Snowflake Model Registry to manage diverse models, including a custom Python utility for data preprocessing that they wish to make available as a model method. Which of the following statements accurately describe capabilities or considerations when logging models and their associated artifacts and methods in the Model Registry?
- A. Option B
- B. Option C
- C. Option A
- D. Option D
- E. Option E
Correct Answer: A,B,D,E 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
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