SnowPro Specialty: Gen AI Question 282
Single answerEvent tablesA team is building a customer-support copilot in Snowflake and wants to troubleshoot intermittent failures in Cortex-powered inference workflows. They need a centralized, queryable record of telemetry such as log, trace, and metric data generated by the application so engineers can investigate issues using SQL. The team also wants to minimize custom pipeline work and keep observability data inside Snowflake for governance. Which Snowflake feature should they use?
- A
Create an event table and configure the application or services to write observability telemetry there for SQL-based analysis
- B
Create a dynamic table that automatically captures trace spans, logs, and metrics from Cortex functions without additional configuration
- C
Use a stream on the application tables because streams persist log and trace history for observability workloads
- D
Store telemetry in a regular permanent table because regular tables automatically provide the same observability semantics as event tables
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use an event table. In Snowflake, event tables are intended to store observability data such as logs, traces, and metrics, making them a strong fit for monitoring and troubleshooting Gen AI applications and services while keeping data governed and queryable in Snowflake. This aligns with Snowflake observability best practices: centralize telemetry, analyze it with SQL, and avoid unnecessary external movement when in-platform visibility is sufficient. By contrast, dynamic tables are for incremental query result maintenance, streams are for CDC, and regular tables are generic storage rather than the purpose-built telemetry destination described in Snowflake documentation for event tables and observability.
- A. Correct.
Correct. Event tables are Snowflake objects designed for observability data, including logs, trace events, and metrics. They provide a centralized place to store telemetry that can then be queried with SQL inside Snowflake. For a Gen AI application, this is an appropriate choice when the team wants in-platform troubleshooting and governance with minimal external pipeline complexity.
- B. Incorrect.
Incorrect. Dynamic tables are used to materialize the results of a query pipeline and keep derived data fresh; they are not the native mechanism for capturing observability telemetry such as logs, traces, and metrics. A common misconception is to treat any automatically refreshed table feature as a monitoring store, but dynamic tables do not replace event tables for observability.
- C. Incorrect.
Incorrect. Streams track change data capture (CDC) on supported objects so downstream processes can consume inserted, updated, or deleted rows. They do not act as a telemetry repository for logs, traces, or metrics. Someone might choose this because streams capture changes over time, but that is different from application observability data.
- D. Incorrect.
Incorrect. A regular table can store any data schema you design, but it does not automatically provide the purpose-built observability model and conventions of an event table. The misconception here is that because all Snowflake tables are queryable, they are interchangeable for observability. In practice, event tables exist specifically to support logging, tracing, and metrics scenarios.