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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?

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

2. A Snowflake developer is tasked with enhancing a daily data pipeline. The pipeline processes raw text descriptions of system events and needs to extract structured information, specifically the (string) and its (string, restricted to 'low', 'medium', 'high', 'critical'). The output must be a strictly formatted JSON object, ensuring data quality for downstream analytics.
Consider the following SQL snippet intended for this transformation:

Which of the following statements are correct regarding this implementation and best practices for using with structured outputs in a data pipeline?

A) Setting 'temperature 'to '0.7 ' is optimal for ensuring the most consistent and deterministic JSON outputs, especially for complex extraction tasks.
B) The 'response_format' correctly defines the expected JSON structure, using 'enum' for 'severity_lever and 'required' to ensure 'event_name' and severity_lever are always present if extracted.
C) The complexity of the JSON schema, particularly deep nesting, does not impact the number of tokens processed and billed for 'AI_COMPLETE Structured Outputs.
D) For all models supported by 'AI_COMPLETE' Structured Outputs, the 'additionalPropertieS field must be set to 'false' in every node of the schema, and the 'required' field must contain all property names.
E) Using 'TRY COMPLETE instead of would allow the pipeline to gracefully handle cases where the model fails to generate a valid JSON response by returning 'NULL' instead of an error.

3. A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

A)

B)

C)

D)

E)

4. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
B) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
C) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
D) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
E) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.

5. A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?

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

Solutions: