SnowPro Specialty: Gen AI Question 3
Single answerSnowflake CortexA retail company stores millions of product reviews in a Snowflake table. The analytics team wants to classify each review as POSITIVE, NEGATIVE, or NEUTRAL directly in Snowflake and then join the results to existing sales dashboards. They want the fastest path to production with minimal infrastructure to manage and no model training. Which approach is the MOST appropriate using Snowflake Cortex?
- A
Use a Cortex LLM function such as SNOWFLAKE.CORTEX.COMPLETE with a prompt that asks the model to return one of the three sentiment labels, and persist the results in a table for downstream SQL joins.
- B
Export the reviews to an external GPU environment, fine-tune a custom sentiment model, and write predictions back to Snowflake because Cortex requires model training for classification tasks.
- C
Create a Snowpark Container Services application first, because Cortex functions cannot be called directly from SQL for text processing workloads.
- D
Use Dynamic Tables only, because they provide built-in sentiment classification without requiring any Cortex or model inference functions.
Show answer and explanation
Correct answer: A
Explanation
This scenario is about selecting the most practical Snowflake-native approach for sentiment classification when the team wants minimal operational overhead and no custom training. Snowflake Cortex is designed for exactly this type of use case: applying AI models directly within Snowflake using SQL, Python, or REST-based patterns depending on the feature. For prompt-based text classification, a Cortex LLM function can be used to instruct the model to output one of a constrained set of labels such as POSITIVE, NEGATIVE, or NEUTRAL. The output can then be persisted and joined to other business data using normal Snowflake SQL workflows. The key best-practice idea is to keep the workload close to the data and avoid unnecessary movement to external infrastructure unless there is a clear need for custom model hosting or specialized tuning. This aligns with Snowflake guidance around Cortex as a managed AI capability for inference inside the Snowflake platform.
- A. Correct.
Correct. Snowflake Cortex provides SQL-accessible AI functions, including LLM inference through functions such as SNOWFLAKE.CORTEX.COMPLETE, which can be used to classify text with prompt-based instructions. For a team seeking minimal infrastructure and no model training, using a Cortex function directly in Snowflake is the most appropriate approach. The results can be stored in a table or view and joined with other Snowflake data using standard SQL.
- B. Incorrect.
Incorrect. This adds unnecessary operational complexity and contradicts the requirement for the fastest path to production with minimal infrastructure and no model training. Cortex is specifically designed to let users invoke foundation models from within Snowflake without managing external GPU environments or training custom models for common text tasks like classification.
- C. Incorrect.
Incorrect. Snowpark Container Services is intended for deploying and running custom containerized applications and models, but it is not required to use Snowflake Cortex. A common misconception is that all AI workloads in Snowflake require custom application hosting. In reality, Cortex functions are designed to be invoked directly from SQL or programmatic interfaces for many inference use cases.
- D. Incorrect.
Incorrect. Dynamic Tables help automate data transformation pipelines, but they do not provide built-in sentiment classification by themselves. They can be used to orchestrate refreshes of derived results, including outputs from Cortex functions, but they are not a replacement for model inference.