Snowflake GES-C01 Valid Dump : SnowPro® Specialty: Gen AI Certification Exam

GES-C01 real exams

Exam Code: GES-C01

Exam Name: SnowPro® Specialty: Gen AI Certification Exam

Updated: Sep 11, 2026

Q & A: 351 Questions and Answers

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Snowflake GES-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro® Specialty: Gen AI Certification Exam
Exam Number:GES-C01
Exam Format:Multiple Choice, Multiple Select
Real Exam Qty:100
Exam Duration:90 minutes
Related Certifications:SnowPro Core Certification
SnowPro Data Engineer
SnowPro Architect
Exam Price:USD 200
Passing Score:70%
Available Languages:English
Certificate Validity Period:2 years
Sample Questions:Free Download GES-C01 valid dump
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:

SectionWeightObjectives
Topic 1: Architecture and Best Practices10-15%- Monitoring and evaluation frameworks
- Security and privacy considerations
- LLM pipeline architecture design
- Cost management strategies
- Performance optimization techniques
Topic 2: Cortex Analyst and Semantic Layer20-25%- Semantic model design and configuration
- Text-to-SQL translation and optimization
- Performance tuning for analytical queries
- Business logic implementation in semantic models
Topic 3: Snowflake Cortex AI Capabilities25-30%- Snowflake Copilot integration
- Model selection and cost optimization
- Cortex AI functions and features
- COMPLETE function usage and parameters
- Secure data handling in AI workflows
Topic 4: Generative AI Fundamentals and Concepts20-25%- Prompt engineering principles
- LLM fundamentals and architectures
- Retrieval-Augmented Generation (RAG) concepts
- Fine-tuning vs. retrieval approaches
- Vector embeddings and similarity search
Topic 5: Data Preparation for Gen AI15-20%- Unstructured data handling
- Document processing and chunking strategies
- Vector stores and embeddings in Snowflake
- Data governance for AI workloads

Snowflake SnowPro® Specialty: Gen AI Certification (GES-C01) — Questions Candidates Actually Ask

The GES-C01 exam, officially known as Snowflake SnowPro® Specialty: Gen AI Certification, is the Snowflake test that leads to the Snowflake Certification certification at the Specialty level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as SnowPro Core Certification, SnowPro Architect, SnowPro Data Engineer.

The GES-C01 exam contains 100 questions, and you have 90 minutes to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.

The passing score for the GES-C01 exam is 70%, and the official registration fee is USD 200. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.

Recommended: SnowPro Core Certification or equivalent hands-on experience with Snowflake platform

Entry requirements can change, so confirm the latest conditions on the official exam page: https://docs.snowflake.com/en/certification/snowpro/specialty/gen-ai.

Yes. A free PDF demo of the Snowflake SnowPro® Specialty: Gen AI Certification questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.

If you take the corresponding GES-C01 exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.

The official Snowflake SnowPro® Specialty: Gen AI Certification outline is organized into 5 domains. The first three are:

  • Cortex Analyst and Semantic Layer — 20-25% of the exam
  • Data Preparation for Gen AI — 15-20% of the exam
  • Generative AI Fundamentals and Concepts — 20-25% of the exam

See the complete exam topics section above for the full outline and the weighting of every domain.

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question #1

A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

  • A. Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
  • B. Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
  • C. Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
  • D. Primary purpose is to translate text. Both document types should use LAYOUT mode.
  • E. Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
  • B. The full text of the natural language questions submitted by the users.
  • C. The exact SQL queries that Cortex Analyst generated in response to user questions.
  • D. Any error messages or warnings that occurred during the processing of the request.
  • E. The complete request and response bodies associated with the application's execution steps.
Reveal Solution  Discussion  0

Correct Answer: B,C,D,E  🗳️

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Question #3

A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

  • A. The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
  • B. If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
  • C. To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
  • D. When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
  • E. For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.
Reveal Solution  Discussion  0

Correct Answer: D,E  🗳️

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Question #4

A data scientist needs to generate vector embeddings for product descriptions stored in a column 'PRODUCT_DESCRIPTION' in the 'PRODUCT_CATALOG' table. They want to use the 'e5-base-v2 model for this task. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.EMBED TEXT 768' function and accurately describes the expected data type of the resulting embedding?

  • A. The query
  • B. The query
  • C. The query
  • D. The query
  • E. The query
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

A data analytics team aims to enhance their understanding of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK. This table has a REVIEW_TEXT column containing raw customer comments and a CUSTOMER_SEGMENT column. The team wants to classify each review into predefined categories and then generate a concise summary of all reviews for each customer segment. Which of the following Snowflake Cortex AI functions and approaches should they use?

  • A. Option E
  • B. Option D
  • C. Option A
  • D. Option C
  • E. Option B
Reveal Solution  Discussion  0

Correct Answer: A,C,D  🗳️

Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).

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