SnowPro Specialty: Gen AI Question 93
Single answerTask-specific functionsA retail company stores millions of customer support tickets in Snowflake and wants to enrich each new ticket as it arrives. The business requirement is to automatically assign one of these labels to every ticket: BILLING, SHIPPING, RETURN, or TECHNICAL. The data engineering team wants the solution to run directly in SQL inside Snowflake, minimize prompt engineering, and produce consistent category values that downstream dashboards can aggregate reliably. Which approach is the MOST appropriate?
- A
Use the task-specific classification function with the predefined set of allowed labels so the model returns one of the business categories directly in SQL.
- B
Use a generic text generation function with a prompt that asks the model to classify the ticket, because free-form generation is the best way to guarantee consistent labels.
- C
Use a summarization function first, then map the summary to a category with a dashboard calculation, because summarization is designed for labeling tasks.
- D
Use an embedding function and store vectors only, because embeddings automatically provide the final business label without any additional logic.
Show answer and explanation
Correct answer: A
Explanation
The best choice is to use a task-specific classification function because the problem is a classic predefined-label classification use case. Snowflake's task-specific functions are intended to simplify common GenAI workloads such as classification, sentiment analysis, extraction, and summarization by reducing the need for complex prompt engineering and by returning output aligned to the task. In an operational pipeline where downstream dashboards expect stable category values, a classification-specific function is more appropriate than free-form generation. By contrast, generic completion functions are more flexible but usually require tighter prompt controls and post-processing to keep outputs consistent. Summarization and embeddings solve different problems: summarization condenses content, and embeddings support semantic comparison or retrieval rather than direct categorical labeling. This aligns with Snowflake best practices of selecting the most specialized function that matches the business task to improve reliability, simplify SQL workflows, and reduce post-processing.
- A. Correct.
Correct. A task-specific classification function is designed for assigning text into predefined categories. In this scenario, the business already knows the target labels and needs reliable, structured output that can be aggregated downstream. Using a classification-oriented function in SQL reduces prompt design complexity and is better aligned with the requirement for consistent category values.
- B. Incorrect.
Incorrect. A generic text generation function can sometimes perform classification through prompting, but it typically returns free-form text and requires more careful prompt design and output handling. That makes consistency harder to enforce for downstream reporting. The misconception is assuming a general-purpose generation function is preferable even when Snowflake provides a task-specific function that is better suited to structured classification.
- C. Incorrect.
Incorrect. Summarization is intended to condense content, not to assign a record into a predefined business class. While a summary might help a human understand a ticket, it does not directly satisfy the requirement for deterministic category assignment. The misconception is treating summarization as a substitute for classification.
- D. Incorrect.
Incorrect. Embeddings convert text into numerical vector representations that are useful for semantic search, similarity, clustering, or as features for downstream models. They do not directly output the required business category by themselves. Additional logic or a classifier would still be needed. The misconception is confusing representation learning with final label assignment.