SnowPro Specialty: Gen AI Question 283
Single answerEvent tablesA team is building a Snowflake-native GenAI chatbot and wants better observability for production troubleshooting. They already collect traces and log messages from the application and need a Snowflake object that can store this telemetry so engineers can run SQL to investigate failed requests, correlate logs with traces, and retain the data under standard table governance controls. Which Snowflake feature should they use?
- A
Create an event table and configure telemetry data collection to write logs, traces, and related observability records into it
- B
Create an external table over staged JSON files because event telemetry can only be queried after being exported from Snowflake
- C
Create a dynamic table because telemetry data for GenAI applications must be materialized through incremental refresh before it can be analyzed
- D
Create a semantic view because logs and traces are exposed only through Cortex Analyst-style business metadata layers
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use an event table. In Snowflake, event tables are built for observability workloads and are used to store telemetry data such as logs and trace events so teams can query and troubleshoot operational behavior with SQL. This is especially relevant for GenAI applications, where engineers often need to inspect request paths, diagnose failures, and correlate application behavior across traces and logs. By contrast, external tables query externally stored files, dynamic tables transform data into derived tables, and semantic views provide business semantics rather than raw telemetry storage. Snowflake documentation on logging, tracing, and event tables describes event tables as the core object for storing and analyzing telemetry data within Snowflake.
- A. Correct.
Correct. Event tables are the Snowflake object designed for observability data such as logs, trace events, and metrics/telemetry records generated by supported Snowflake telemetry features. They let teams use standard SQL for analysis and apply familiar governance patterns such as access control and retention management. In a GenAI application, this is the appropriate choice when the goal is to troubleshoot requests, correlate traces and logs, and analyze telemetry directly in Snowflake.
- B. Incorrect.
Incorrect. External tables are for querying data stored in external cloud storage, not the primary Snowflake feature for storing native telemetry generated for observability. A candidate might choose this because telemetry is often exported to object storage in other platforms, but Snowflake event tables are specifically intended to store and query this operational data inside Snowflake.
- C. Incorrect.
Incorrect. Dynamic tables are used for pipeline-style transformations and incremental processing of derived datasets. They are not the native storage construct for application observability telemetry. This distractor reflects the misconception that any continuously arriving data should first be materialized through dynamic tables, which is not required for event-table-based telemetry analysis.
- D. Incorrect.
Incorrect. Semantic views are intended to provide business-friendly semantic layers over data for analytics use cases, not to store raw operational telemetry such as logs and trace events. Someone might pick this if they confuse GenAI analytics features with observability features, but semantic views do not replace event tables for telemetry storage.