Snowflake GES-C01 Dumps - Pass Exam With Ease [2026]

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q317-Q322):

NEW QUESTION # 317
A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

Answer: B,E

Explanation:
Option A is correct because the CORTEX_ENABLED_CROSS_REGION account parameter is used to enable cross-region inference. Setting it CORTEX_ENABLED_CROSS_REGION would permit inference requests to be processed in any AWS US region, such as N. Virginia, from a local AWS US region like Ohio. to 'Aws us' Option B is incorrect because user inputs, service generated prompts, and outputs are explicitly not stored or cached during cross-region inference. Option C is incorrect as cross-region inference is not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option D is correct because the sources indicate that AI COMPLETE (llama4-maverick) is natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference within AWS US regions, making it a valid target for the account in US East (Ohio). Option E is incorrect because latency between regions depends on the cloud provider infrastructure and network status, and Snowflake recommends testing specific use cases with cross-region inference enabled.


NEW QUESTION # 318
A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry. The goal is to deploy this model on Snowpark Container Services (SPCS) for scalable inference. The PyCaret model relies on the 'pycaret' and 'scipy' Python libraries, and the data scientist has local 'sample data.csv' for inferring the model's signature. Which statements are crucial for successfully logging this custom model for eventual SPCS deployment?

Answer: C,E

Explanation:
Option B is correct because for models deployed to Snowpark Container Services, dependencies are typically obtained from 'conda- forge' or PyPl. Therefore, 'pip_requirements' is the appropriate way to specify PyPl packages, and explicitly setting ['SNOWPARK_CONTAINER_SERVICEST guides the deployment target. Option C is correct because either (a Pandas or Snowpark DataFrame) or a model 'signature' must be provided to the method for input validation and to infer the model's input signature. Option A is incorrect because 'conda_dependencieS in 'log_model' assumes the Snowflake channel for warehouse deployment, whereas for SPCS, it's 'conda-forge' or PyPl. Mixing channels or assuming Snowflake channel for SPCS is incorrect. Option D is incorrect. The 'use_gpu" option is used when 'loading' a model version Cmv.load(options={'use_gpu': to enable GPU-specific loading logic, not when logging the model. GPU requests for inference are specified when creating the service. Option E is incorrect. Snowflake recommends using only 'conda_dependencieS or 'pip_requirements' , not both, to avoid potential compatibility issues during the container image build.


NEW QUESTION # 319
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: D

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 # 320
A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

Answer: C,D,E

Explanation:
Option A is incorrect because Structured Outputs do not incur additional compute cost for the overhead of verifying each AI_COMPLETE token against the supplied JSON schema, though the number of tokens processed (and thus billed) can increase with schema complexity. Option B is correct because for complex reasoning tasks, it is recommended to use the most powerful models and explicitly add 'Respond in JSON' to the prompt to optimize accuracy. Option C is correct as for OpenAI (GPT) models, the schema has specific requirements: response_format must be set to in every node, and the required field must include the names of every property in the schema. Option D additional Properties false is incorrect because AI_COMPLETE verifies each generated token against the JSON schema to ensure conformity, and if the model cannot generate a response that matches the schema, it will result in a validation error. Option E is correct as setting the option to e is recommended for temperature the most consistent results, regardless of the task or model, especially for structured outputs.


NEW QUESTION # 321
An engineering team is building an advanced analytics pipeline where daily customer activity vectors (each 512 dimensions) are compared against a set of known activity patterns using VECTOR_L2_DISTANCE The pipeline is orchestrated using Snowflake Tasks. Which operational best practice or limitation should the team consider when processing these vector distances at scale?

Answer: E

Explanation:
Option A is incorrect. Snowflake recommends executing queries that call Cortex AI SQL functions with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Snowpark-optimized warehouses are generally recommended for workloads with large memory requirements or dependencies on specific CPU architectures, not as a general performance booster for Cortex AI functions. Option B is incorrect.
TRY COMPLETE
is a function designed for LLM completions (like
COMPLETE
) to return
NULL
instead of an error, and it is not applicable to vector distance functions like VECTOR_L2_DISTANCE Option C is incorrect. The VECTOR data type is not supported for use with dynamic tables. Option D is incorrect. Bind variables are not supported with the VECTOR data type. Option E is correct. Snowflake's documentation explicitly states that queries calling Cortex AI SQL functions, which includes vector similarity functions, perform optimally on smaller warehouses (no larger than MEDIUM) because larger warehouses do not increase performance for these specific functions.


NEW QUESTION # 322
......

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