2026 GES-C01: Updated SnowPro® Specialty: Gen AI Certification Exam Study Group

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q233-Q238):

NEW QUESTION # 233
A security administrator is implementing strict model access controls for Snowflake Cortex LLM functions, including those accessed via the Cortex REST API. By default, the 'SNOWFLAKE.CORTEX USER' database role is granted to the 'PUBLIC' role, allowing all users to call Cortex AI functions. To enforce a more restrictive access policy, the administrator revokes 'SNOWFLAKE.CORTEX USER from 'PUBLIC'. Which of the following actions must the administrator take to ensure specific roles can 'still' make Cortex REST API requests, and what are the implications?

Answer: B

Explanation:
To send a REST API request to Cortex, the default role of the calling user must be granted the 'SNOWFLAKE.CORTEX_USER database role. By default, this role is granted to 'PUBLIC', but it can be revoked. If revoked, the 'CORTEX USER role must be explicitly granted to other account roles, which are then granted to users. The 'CORTEX_USER role cannot be granted directly to a user. The 'CORTEX MODELS_ALLOWLIST' parameter can also be used to restrict which models are accessible at the account level for Cortex functions, including those accessed via the REST API. Therefore, option B correctly outlines the required actions and an additional control. Options A, C, D, and E are incorrect as they misrepresent the access control mechanisms or requirements for Cortex REST API.


NEW QUESTION # 234
A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.

Answer: B

Explanation:
The scenario requires statefulness (multi-turn conversation), creative variation in responses, and safety (filtering harmful content). Option B correctly demonstrates a multi-turn conversation by passing a history of user and assistant messages in the prompt array. It uses a of which allows for creative variation in the output, as higher temperatures result in more diverse output. It also sets guardrails temperature 0.8, to enable content filtering. Option A's prompt is single-turn, not multi-turn. Option C uses a single-turn prompt and disables guardrails. to TRUE Option D uses a of which produces deterministic results and lacks creative variation. Option E has an incorrect conversation temperature 0.0, history structure with two consecutive 'user' roles without an 'assistant' response in between, which is not supported for stateful conversations.


NEW QUESTION # 235
A new ML Engineer, 'data_scientist_role' , has been assigned to a project involving custom machine learning models in Snowflake. They need to gain the necessary permissions to perform the following actions related to Snowflake Model Registry and Snowpark Container Services: 1. Log a custom model into a specified schem a. 2. Deploy that model to an existing Snowpark Container Service compute pool. 3. Call the deployed model for inference using SQL. Which of the following SQL commands grant the 'minimal' required privileges to the for these actions, assuming the compute pool and image repository already exist and are appropriately configured?

Answer: A,E

Explanation:
Option A is correct because the 'CREATE MODEL' privilege on the target schema is required to log a new model (which creates a model object) in the Snowflake Model Registry. Option D is correct because deploying a model to a Snowpark Container Service creates a service object within a schema, which requires the 'CREATE SERVICE privilege on that schema. The role would also implicitly need 'USAGE on the specified compute pool. Option B is incorrect. While 'USAGE ON DATABASE is generally needed for accessing objects within a database, it's a broader prerequisite and not specifically a minimal privilege for the direct model registry actions of logging, deploying, and calling the model. Option C is incorrect because 'CREATE COMPUTE POOL' is for creating the compute pool itself, not for deploying a service 'to' an existing one. The role would need 'USAGE' on the existing compute pool, but not the right to create it from scratch for this scenario. Option E is incorrect because 'READ ON IMAGE REPOSITORY is required for the 'service' to pull the image from the repository, but the question asks for privileges for the to perform the 'actions' of logging, deploying, and calling. While the role might need to manage or verify the image, this isn't a direct privilege for the user's interaction with the deployed model in the same way 'CREATE MODEL' or 'CREATE SERVICE are.


NEW QUESTION # 236
A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

Answer: B

Explanation:
To ensure that LLM responses adhere to a predefined JSON structure, the 'AI_COMPLETE function's 'response_format' argument, which accepts a JSON schema, is the most effective and direct method. This mechanism enforces the structure, data types, and required fields, significantly reducing the need for post-processing and ensuring deterministic, high-quality output. The AI-Infused Data Pipelines with Snowflake Cortex blog highlights asking the LLM to create a JSON object for maximizing utility. While setting 'temperature' to 0 can improve consistency, it does not enforce a specific schema. Prompt engineering (Option A) can help but does not guarantee strict adherence. Using multiple extraction calls (Option D) is less efficient and robust for extracting multiple related fields than a single 'AI_COMPLETE call with a structured output schema. Snowflake Cortex does not automatically infer and enforce a JSON schema without explicit configuration (Option E).


NEW QUESTION # 237
An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?

Answer: B,C

Explanation:


NEW QUESTION # 238
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