DSA-C03 Exam Dumps | You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through

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Snowflake.DSA-C03.v2026-01-08.q143
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Question 23/143

You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A. Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.

B. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.

C. Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.

D. Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.

E. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.

Correct Answer: D

Storing models in external stages with versioning allows you to easily manage different model versions. Snowflake metadata points to the correct version, and UDFs can load them. Time Travel is useful, but is not ideal for large binary files. Option A is possible, but leads to potentially large and unwieldy Snowflake tables. Option C is not recommended as manual processes can lead to human errors and overwriting active models directly without proper model management creates deployment risks. Deleting older models (option E) prevents rollback.

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