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

Updated: Jun 19, 2026

Q & A: 351 Questions and Answers

GES-C01 Braindumps VCE
  • Exam Code: GES-C01
  • Exam Name: SnowPro® Specialty: Gen AI Certification Exam

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company is developing a Streamlit application leveraging Snowflake Cortex Analyst for natural language querying over sales data.
They want to implement a robust multi-turn conversational experience where users can ask follow-up questions. Which of the following statements accurately describe the design and cost implications of supporting multi-turn conversations in Cortex Analyst? (Select all that apply)

A) The cost for Cortex Analyst's multi-turn conversational support is primarily based on the number of messages processed, and the number of tokens within each message does not directly affect the per-message cost.
B) Cortex Analyst supports multi-turn conversations by simply passing the entire conversation history directly to every LLM call within its agentic workflow, which is the most efficient method for maintaining context.
C) An internal LLM summarization agent is automatically employed by Cortex Analyst before its original workflow to reframe follow-up questions based on conversation history, optimising LLM processing for each agent.
D) Developers can manually implement multi-turn conversations in their applications by using the
E) When an LLM judge is used to evaluate the summarization quality for multi-turn conversations, a smaller model like Llama 3.1 8B is generally preferred over Llama 3.1 70B to minimise latency, even if it leads to a slightly higher error rate in rewritten questions.


2. A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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


3. A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A) The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
B) Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.
C) Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.
D) Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.
E) Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.


4. A company is using Snowflake AI Observability to evaluate a summarization application. The application utilizes SNOWFLAKE. CORTEX. COMPLETE for LLM inference and a custom Python component for text pre-processing. The team is particularly interested in tracking detailed cost breakdowns and assessing the factual correctness of the LLM-generated summaries. Which of the following statements accurately describe the cost implications and evaluation metric capabilities in this scenario?

A) For evaluating summarization tasks, the 'context relevance' score is the most important metric, as it directly assesses the quality of the LLM's output against the source document.
B) AI Observability incurs charges for the LLM judges invoked via COMPLETE (SNOWFLAKE .CORTEX) calls to compute evaluation metrics, in addition to warehouse charges for managing runs and queries.
C) To measure the factual correctness of LLM-generated summaries based on original input and avoid hallucinations, the 'factual correctness' and 'comprehensiveness' metrics can be used during evaluations.
D) The cost of AI Observability is primarily determined by the number of messages processed, and the number of tokens in each message does not affect the cost, ensuring predictable pricing.
E) The ORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the primary tool to monitor the credit consumption specifically for AI Observability evaluations and LLM judge usage.


5. 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 E
C) Option D
D) Option A
E) Option C


Solutions:

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

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