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[Hardware] Get Latest Snowflake GES-C01 Practice Test For Quick Preparation

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【Hardware】 Get Latest Snowflake GES-C01 Practice Test For Quick Preparation

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q284-Q289):NEW QUESTION # 284
A data engineer is designing a new feature for a Retrieval Augmented Generation (RAG)-based application in Snowflake. They plan to store document embeddings and perform semantic similarity searches to retrieve relevant context for an LLM. Which of the following statements about using the VECTOR data type and related functions in Snowflake are true? (Select all that apply.)

  • A. Option D
  • B. Option B
  • C. Option A
  • D. Option E
  • E. Option C
Answer: C,D,E
Explanation:


NEW QUESTION # 285
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. 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.
  • B. 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.
  • 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. 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.
  • E. The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
Answer: C,D,E
Explanation:
Option B is correct: when Cortex Guard is enabled and a response is blocked, the model's output is replaced by a message (defaulting to 'Response filtered by Cortex Guard'), and only 'guard_tokens' are counted as input tokens for Cortex Guard's processing, in addition to the primary LLM's prompt and completion tokens. Option C is correct as the argument allows customization of the filtered response message for both 'COMPLETE' and the REST API. Option E is correct because Cortex Guard is built with Meta's Llama Guard 3 and its usage is billed separately as 'guard_tokens' in addition to the 'COMPLETE function cost. Option A is incorrect because while guard_tokens' are billed, it's specifically for the guardrail processing, and the 'unsafe' completion tokens are not returned or billed as such, rather replaced by a filtered message. Option D is incorrect because Cortex Guard requires explicit enablement by setting 'guardrails' to 'TRUE'.

NEW QUESTION # 286
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.
Answer: A
Explanation:
Option A is correct. This sequence of commands first sets an account-level allowlist for 'mistral-large? and 'snowflake-arctic' , thereby restricting general access to other models for plain-name string lookups. The 'CALL ensures the changes are applied. It then explicitly grants the DATA SCIENCE_TEAM ROLES access to the 'claude-3-5-sonnet' model object using its dedicated application role 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5-SONNET"'. This ensures 'claude-3-5-sonnet is accessible only to that specific role and not globally through the allowlist, fulfilling the granular access requirement. Option B is incorrect because 'ALTER ACCOUNT operations require the 'ACCOUNTADMIW role, not 'SYSADMIN'. Additionally, setting to 'claude-3-5-sonnet' would make it globally available, contradicting the requirement for restricted access. Option C is incorrect because model-level RBAC for base models in 'SNOWFLAKE.MODELS' is primarily applied using application roles (e.g., 'CORTEX-MODEL-ROLE'), not directly with 'GRANT USAGE ON MODEL'. Option D is incorrect. While clearing the allowlist is a valid part of a strategy, 'GRANT USAGE ON ALL MODELS IN SCHEMA SNOWFLAKE.MODELS' would grant access to 'all' models in that schema, which contradicts the requirement for 'claude-3-5-sonnet' to be exclusive to the data science team and not generally available. Option E is incorrect because SALTER ACCOUNT requires the 'ACCOUNTADMIN& role, not 'SECURITYADMIN', and setting the allowlist to 'claude-3-5-sonnet' would make it generally available, violating the isolation requirement.

NEW QUESTION # 287
A Gen AI developer is implementing a Cortex Search Service for a RAG application and needs to configure the text splitting for optimal performance using SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER Which of the following statements represent best practices or outcomes when applying text splitting with this function for Cortex Search in a RAG scenario? (Select all that apply)
  • A. Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results in Cortex Search.
  • B. Optimal text splitting using this function ensures that the number of input tokens precisely equals the number of output tokens for subsequent LLM calls, thereby minimizing compute costs.
  • C. The function automatically enriches each text chunk with relevant metadata about its original document, such as author and creation date, for enhanced filtering capabilities in Cortex Search.
  • D. Smaller chunk sizes generally lead to higher retrieval precision for a given query in a RAG system.
  • E. Even when using embedding models with larger context windows (e.g., 8000 tokens), a smaller chunk size is typically preferred for improved retrieval and downstream LLM response quality.
Answer: A,D,E
Explanation:
Options A, B, and C are correct. Snowflake explicitly recommends splitting text in a search column into chunks of no more than 512 tokens for best search results with Cortex Search. Research indicates that smaller chunk sizes typically result in higher retrieval precision for a given query and improved downstream LLM response quality. This practice is recommended even when longer-context embedding models, such as

with an 8000 token context window, are available, because smaller chunks provide more precise retrieval and more relevant context for the LLM. Option D is incorrect; the sources do not mention that SPLIT_TEXT_RECURSIVE_CHARACTER automatically enriches chunks with metadata. This would typically require additional data processing steps. Option E is incorrect; the primary goal of text splitting is to optimize retrieval and LLM response quality, not to balance input and output token counts for cost reasons. While token counts influence costs, the 512-token recommendation is driven by quality considerations.

NEW QUESTION # 288
A Gen AI Specialist is responsible for maintaining a Cortex Analyst-powered application. They have defined a semantic model that includes a Verified Query Repository (VQR) to guide user interactions. The application front-end uses the Suggested Questions feature to help users get started. The specialist wants to ensure that a specific set of critical, verified business questions are always displayed to users, regardless of their prior input or the semantic similarity to their current query. Which of the following configuration steps in the semantic model YAML will achieve this requirement?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: C
Explanation:


NEW QUESTION # 289
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