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Actual exam question for Snowflake's GES-C01 exam Question #: 17 Topic #: 1
A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?
Option C is the most direct and efficient approach for continuously augmenting data with sentiment scores in a Snowflake pipeline. is a task-specific AI function designed for this purpose, returning an overall sentiment score for English-language text. SNOWF LAKE .CORTEX.SENTIMENT Integrating it directly into a task that monitors a stream allows for automated, incremental processing of new data as it arrives in the stage. The source explicitly mentions using Cortex functions in data pipelines via the SQL interface. Option A is plausible, but calling SENTIMENT directly in SQL within a task (Option C) is simpler and avoids the overhead of a Python UDF if the function is directly available in SQL, which it is. Option B, using a dynamic table, is not supported for Snowflake Cortex functions. Option D, while powerful for custom LLMs, is an over-engineered solution and introduces more complexity (SPCS setup, custom service) than necessary for a direct sentiment function. Option E describes a manual, non- continuous process, which contradicts the requirement for an automated pipeline.
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