ARA-C01 Question 312
Single answerSnowflake Connector for Google AnalyticsA retail company uses the Snowflake Connector for Google Analytics to load Universal Analytics reporting data into Snowflake each morning for executive dashboards. Recently, the marketing team added several new custom dimensions and metrics in Google Analytics. The next scheduled load completed without errors, but analysts found that the new fields were not available in Snowflake. As the Snowflake architect, you need to restore availability of the new fields with minimal disruption to the existing pipeline. What should you do?
- A
Re-run the connector using a full historical backfill; the connector automatically detects new Google Analytics dimensions and alters the target table schema during reload.
- B
Update the connector configuration to include the new dimensions and metrics, then recreate or refresh the destination table/schema mapping used by the connector before running the load again.
- C
Create a Snowpipe on the target table so that newly added Google Analytics fields are ingested automatically when the connector writes files to stage.
- D
Enable schema evolution on the target table; the connector will populate newly added Google Analytics dimensions and metrics without any connector configuration changes.
Show answer and explanation
Correct answer: B
Explanation
The key issue is that the Snowflake Connector for Google Analytics depends on the dimensions and metrics defined in its configuration and target mapping. When the source system adds new custom dimensions or metrics, those fields do not appear automatically in Snowflake unless the connector is updated to request them and the destination structure is aligned to store them. Architecturally, the right response is to modify the connector configuration and refresh or recreate the schema/table mapping as needed, then rerun the load. Options involving Snowpipe or generic schema evolution confuse downstream ingestion mechanics with source extraction configuration. Best practice is to treat changes to Google Analytics report definitions as controlled schema changes: validate API field availability, update the connector configuration, adjust target objects, and then rerun affected loads. This aligns with Snowflake documentation for connectors and with standard ELT design principles where source field selection and target schema management are explicit rather than automatic.
- A. Incorrect.
Incorrect. The Snowflake Connector for Google Analytics does not simply discover and persist newly added Google Analytics dimensions and metrics into Snowflake target tables through an automatic historical reload and schema alteration process. The connector relies on configured reports/fields, so adding new GA fields typically requires updating what the connector requests and how the target structure is defined.
- B. Correct.
Correct. When new dimensions or metrics are introduced in Google Analytics, the connector configuration must be updated so those fields are included in the API extraction. In practice, the destination schema or table definition used by the connector also needs to be refreshed or recreated so the new columns exist in Snowflake. This is the least disruptive approach because it preserves the pipeline pattern while explicitly incorporating the new fields.
- C. Incorrect.
Incorrect. Snowpipe is used for auto-ingesting files from stages into Snowflake, but it does not change what the Google Analytics connector extracts from the source API, nor does it automatically add missing business columns to the target table. This option reflects a common misconception that ingestion automation solves source schema selection problems.
- D. Incorrect.
Incorrect. Schema evolution applies in supported loading scenarios such as certain file-loading patterns, but it does not eliminate the need to update the Google Analytics connector's field selection. If the connector is not configured to request the new dimensions and metrics from Google Analytics, Snowflake has no new data to evolve into the table.