SnowPro Specialty: Gen AI Question 279
Single answerTracingA team has deployed a customer support chatbot in Snowflake using Cortex Agents and wants to troubleshoot why some responses are slow and occasionally use the wrong retrieved context. They need to inspect each step of a request, including retrieval and model execution, and compare behavior across requests without changing the application logic. Which approach should they use?
- A
Enable tracing for the application and analyze the generated trace data to inspect spans for steps such as retrieval and model calls
- B
Increase the virtual warehouse size so query history captures more detailed information about each agent step
- C
Review only ACCESS_HISTORY because it records the prompts, retrieved chunks, and model latency for each agent request
- D
Create a dynamic table over the chatbot response table so each intermediate step is materialized for debugging
Show answer and explanation
Correct answer: A
Explanation
The best answer is to enable and use tracing. In Snowflake, tracing provides observability data that helps developers understand how requests move through an application, including step-level execution details. For Gen AI scenarios such as Cortex Agents, tracing is the most appropriate tool when the goal is to diagnose latency, inspect retrieval and model-execution behavior, and compare requests over time without modifying business logic to add custom logging everywhere. By contrast, query history, ACCESS_HISTORY, and dynamic tables each serve different purposes: SQL monitoring, governance/auditing, and data pipeline transformations. Snowflake observability guidance distinguishes tracing from logging and metrics: tracing is specifically for following the path of a request and examining spans across operations. This makes it the practical choice for troubleshooting multi-step Gen AI application behavior.
- A. Correct.
Correct. Tracing is designed to capture execution details across application workflows, including step-level visibility through spans. For Gen AI applications, this is the right mechanism when the team needs observability into operations such as retrieval and model invocation and wants to compare request behavior without rewriting the app to log every step manually.
- B. Incorrect.
Incorrect. Increasing warehouse size may affect performance of warehouse-backed operations, but it does not provide step-by-step observability into an agent workflow. Query history is useful for SQL execution monitoring, not for full request tracing across Gen AI application components such as retrieval and model calls.
- C. Incorrect.
Incorrect. ACCESS_HISTORY is focused on data access governance and auditing, not end-to-end Gen AI workflow inspection. A common misconception is that governance metadata can substitute for observability telemetry. It does not provide the span-level execution breakdown needed to troubleshoot retrieval quality and model latency in an agent flow.
- D. Incorrect.
Incorrect. Dynamic tables are for incremental table transformations and pipeline maintenance, not application tracing. Materializing outputs into tables can help with some downstream analysis, but it does not automatically capture the intermediate execution spans, timings, and request structure needed for debugging a chatbot workflow.