GES-C01 Exam Dumps | A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry.

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Question 115/171

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?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E

Correct Answer: B,C

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.

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