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  1. Home
  2. Snowflake Certification
  3. GES-C01 Exam
  4. Snowflake.GES-C01.v2026-03-27.q130 Dumps
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Question 61

A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

Correct Answer: B
Snowflake explicitly recommends using an X-Small, Small, or Medium warehouse for Document AI. Scaling up the warehouse does not increase the speed of query processing for Document AI but can lead to unnecessary costs. This directly addresses the scenario of a 'LARGE warehouse running continuously and contributing to high bills. Option A is incorrect because while 'METERING DAILY HISTORY is used for cost tracking, Document AI's service-side usage appears under 'AI_SERVICES , not 'WAREHOUSE_METERING' for the AI service component itself. 'WAREHOUSE METERING' would show general warehouse costs, not specifically tied to Document AI's compute portion. Option C is incorrect because Document AI (using ' !PREDICT) incurs 'AI Services compute' costs based on 'time spent actually using these resources' (8 Credits per hour of compute), not per token. Option D is not necessarily accurate guidance; SAI PARSE DOCUMENT is a separate Cortex AI SQL function for document processing, billed per page, while Document AI's '!PREDICT is part of a Document AI model build. Replacing it without a full re- evaluation of the workflow might not be optimal or directly cost-efficient for an established pipeline. Option E is incorrect because the view tracks Document AI processing activity, including '!PREDICT calls.
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Question 62

A data team is deploying a new customer service chatbot using Snowflake Cortex Analyst. To accurately forecast and optimize their costs, the team needs to understand how Cortex Analyst billing works. Which of the following statements accurately describe the cost considerations for Snowflake Cortex Analyst?

Correct Answer: B,E
Option B is correct because Cortex Analyst credit rate usage is based on the number of messages processed, specifically 67 Credits per 1 ,000 messages or 0.067 Credits per message. Option E is correct as only successful responses (HTTP 200) from Cortex Analyst are counted towards the credit usage. Option A is incorrect because the number of tokens in each message does not affect the cost for Cortex Analyst; this is a billing model for other Cortex LLM functions, but not Cortex Analyst. Option C is incorrect; Cortex Analyst runs on Snowflake- managed compute resources as a Serverless Feature, meaning virtual warehouse compute is not directly billed for its operations in the same way as a user-managed warehouse. Option D is incorrect. While Cloud Services compute generally has a 10% daily warehouse cost adjustment, Serverless Features (which include Cortex Analyst) do not contribute to this adjustment.
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Question 63

An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

Correct Answer: A,B,C
Option A is correct because the 'run' method of a 'ModelVersion' object is used to perform inference on models deployed to Snowpark Container Services via the Python API. Option B is correct because Snowflake Model Serving creates SQL service functions, named as 'service_name!method_name', to act as a bridge from SQL to the model running in SPCS. Option C is correct because if ingress is enabled during service creation, the model can be invoked via a dedicated HTTP endpoint. Option D is incorrect because this approach would bypass the deployed Snowpark Container Service entirely, and models deployed to SPCS are intended to be run there, not re-loaded into a standard UDF runtime, which might have different environments or resource constraints. Option E is incorrect because is a built-in Snowflake Cortex LLM function, not a mechanism to call a custom model deployed via the Model Registry and Snowpark Container Services. The model argument for Cortex functions expects specific pre-defined model names, not custom service names.
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Question 64

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?

Correct Answer: A
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Question 65

A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI COMPLETE function call is essential for achieving this structured output requirement?

Correct Answer: C
'AI_COMPLETE Structured OutputS (and its predecessor 'COMPLETE Structured Outputs') specifically allows supplying a JSON schema as the 'response_format' argument to ensure completion responses follow a predefined structure. This significantly reduces the need for post-processing and enables seamless integration with systems requiring deterministic responses. The JSON schema object defines the structure, data types, and constraints, including required fields. While prompting the model to 'Respond in JSON' can improve accuracy for complex tasks, the 'response_format' argument is the direct mechanism for enforcing the schema. Option A is a form of prompt engineering, which can help but does not guarantee strict adherence as 'response_format' does. Option B controls randomness and length, not output structure. Option D is less efficient for extracting multiple related fields compared to a single structured output call. Option E's 'guardrails' are for filtering unsafe or harmful content, not for enforcing output format.
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