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

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Snowflake GES-C01 試験シラバストピック:

Snowflake SnowPro® Specialty: Gen AI Certification 認定 GES-C01 試験問題：

問題 #1

An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?

A. Option E
B. Option D
C. Option C
D. Option B
E. Option A

問題 #2

A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?

A. Option E
B. Option D
C. Option C
D. Option B
E. Option A

問題 #3

Which of the following is a 'direct cause' of this error related to missing schema-level privileges, assuming the model build name is unique and the warehouse is active and accessible?

A.

B.

C.

D.

E.

問題 #4

A data team is designing a new Cortex Analyst application and wants to ensure optimal performance, accuracy, and user experience for text-to-SQL conversions. They are particularly interested in how custom instructions interact with other semantic model features and LLM functionalities. Which of the following statements about using in Cortex Analyst are accurate?

A. Custom instructions are primarily used to define new logical tables or dimensions within the semantic model, effectively extending the data model at runtime.
B. The 'custom_instructions' in a semantic model directly influence the underlying Large Language Model (LLM) to generate SQL queries that align with specified business context or formatting preferences.
C. Using detailed 'custom_instructions' can help mitigate issues where the LLM might struggle with domain-specific terminology or complex business logic not explicitly defined in column descriptions.
D. The presence of 'custom_instructions' in a semantic model can potentially increase the token count for Cortex Analyst requests, as the instructions are passed as additional context to the LLM.
E. When both 'custom_instructionS and a highly relevant 'verified_query' exist for a user's question, Cortex Analyst will always prioritize the directives from the 'custom_instructions' over the SQL provided in the 'verified_querv'.

問題 #5

Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?

A. Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
B. Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.
C. Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
D. Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
E. Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.

解説：