SnowPro Specialty: Gen AI Question 4
Single answerSnowflake CortexA product support team stores customer cases in a Snowflake table with columns CASE_ID, PRODUCT_LINE, CASE_TEXT, and CREATED_AT. They want analysts to run SQL queries that automatically generate a short summary of each case and classify sentiment as positive, neutral, or negative. The solution must stay inside Snowflake, minimize operational overhead, and avoid building or managing external model endpoints. Which approach best meets these requirements?
- A
Use Snowflake Cortex functions such as SUMMARIZE on CASE_TEXT and SENTIMENT on CASE_TEXT directly in SQL queries or views.
- B
Export CASE_TEXT to an external LLM service through a custom application, store the responses back in Snowflake, and query the enriched table.
- C
Train a custom Snowpark ML model for summarization and sentiment analysis, then deploy it as a user-managed service outside Snowflake.
- D
Create a dynamic table that automatically infers summaries and sentiment labels without calling any AI function, because dynamic tables include built-in text understanding.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowflake Cortex functions directly in SQL. Cortex is designed to let teams apply AI capabilities such as summarization and sentiment analysis within Snowflake without exporting data to external services or standing up separate inference infrastructure. This aligns with common Snowflake best practices: keep data in-platform when possible, reduce operational complexity, and expose reusable logic through SQL queries or views for analysts. External LLM integrations and custom model deployments may be valid in other scenarios, but they do not best satisfy the requirements here. Dynamic tables can help operationalize recurring transformations, but they are orchestration objects, not AI features by themselves; they would need to call Cortex functions to produce the desired outputs.
- A. Correct.
Correct. Snowflake Cortex provides built-in AI functions that can be invoked directly from SQL for common generative AI and text analysis tasks. Using functions such as SUMMARIZE and SENTIMENT keeps processing inside Snowflake, reduces integration complexity, and avoids managing external endpoints. This matches the requirement for low operational overhead and analyst-friendly SQL access.
- B. Incorrect.
Incorrect. Although this can work technically, it violates the stated goal of minimizing operational overhead and avoiding external model endpoint management. It also adds data movement, integration, security, and latency considerations that Snowflake Cortex is designed to reduce for in-platform AI use cases.
- C. Incorrect.
Incorrect. A custom Snowpark ML approach may be appropriate for specialized predictive models, but it is unnecessarily complex for standard summarization and sentiment tasks already supported through Cortex functions. It also conflicts with the requirement to avoid building or managing external serving infrastructure.
- D. Incorrect.
Incorrect. Dynamic tables help automate data transformation pipelines, but they do not provide built-in generative AI or text understanding by themselves. To derive summaries or sentiment, you would still need to call the relevant Cortex functions within the transformation logic.