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

GES-C01 real exams

Exam Code: GES-C01

Exam Name: SnowPro® Specialty: Gen AI Certification Exam

Updated: Aug 11, 2026

Q & A: 351 Questions and Answers

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Snowflake GES-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Prompt Engineering- Optimization of prompts for LLM outputs
- Prompt design techniques
Topic 2: Generative AI Fundamentals- Model capabilities and limitations
- Core concepts of generative AI and LLMs
Topic 3: Embeddings, Vector Search & RAG- Embeddings fundamentals
- Retrieval-Augmented Generation (RAG) workflows
- Vector search in Snowflake ecosystem
Topic 4: Data Governance & Security- Data privacy and access controls
- Responsible use of AI in enterprise environments
Topic 5: Model Evaluation & Responsible AI- Bias, fairness, and explainability considerations
- Evaluation metrics for LLM outputs
Topic 6: Use Cases & Solution Design- Enterprise AI application patterns in Snowflake
- End-to-end GenAI solution architecture
Topic 7: Snowflake AI & Cortex- Snowflake Cortex capabilities
- AI functions and services in Snowflake

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Data Scientist has a pre-trained PyCaret model and wants to log it into the Snowflake Model Registry for inference. The model requires specific versions of and 'scipy' , and a configuration file 'my_config.json' needs to be packaged with the model for use during inference. Assuming 'sp_session' is an active Snowpark Session, is an instance of 'PyCaretModel' , and 'train_features' is a Pandas DataFrame for which of the following code snippets correctly logs this custom PyCaret model into the Snowflake Model Registry?

A)

B)

C)

D)

E)


2. An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?

A) Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.
B) The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.
C) To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
D) Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.
E) When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.


3. A financial institution uses Snowflake to process an average of 5 million PDF documents (average 10 pages each) monthly for regulatory compliance using AI_PARSE_DOCUMENT in LAYOUT mode. They are monitoring costs and considering performance optimizations. Given this scenario, which of the following statements about cost and performance for AI_PARSE_DOCUMENT are true?

A) For optimal results with multilingual documents, the LAYOUT mode supports a broader range of languages than OCR mode.
B) Selecting a 'LARGE' or 'X-LARGE' standard warehouse will significantly improve the processing speed and reduce the per-page cost for AI_PARSE_DOCUMENT.
C) To reduce costs, the team should aim to process documents in batches, ensuring each query processes a maximum of 1 ,000 documents to avoid errors.
D) If a document is a HTML file, the billing is based on processing each chunk of 3,000 characters as a page.
E) The primary billing metric for in LAYOUT mode is the number of tokens processed, which includes both input and output tokens.


4. A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml'' file for a long- running service that uses this image, custom environment variables, and requires external access?

A)

B)

C)

D)

E)


5. A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

A) Option A
B) Option E
C) Option D
D) Option C
E) Option B


Solutions:

Question # 1
Answer: E
Question # 2
Answer: C
Question # 3
Answer: A,C,D
Question # 4
Answer: A,C,E
Question # 5
Answer: E

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