ARA-C01 Question 311
Single answerSnowflake Connector for Google AnalyticsA retail company uses the Snowflake Connector for Google Analytics to ingest daily web traffic data into Snowflake for executive dashboards. The marketing team reports that yesterday's conversion metrics are incomplete each morning and only appear to stabilize later in the day. The architect needs a solution that improves data completeness while keeping the ingestion process automated and maintainable. Which approach is the most appropriate?
- A
Modify the ingestion schedule so the connector re-loads a rolling lookback window of recent dates, allowing late-arriving Google Analytics data to be refreshed automatically.
- B
Increase the Snowflake virtual warehouse size used by the connector so that Google Analytics exports finalize earlier and the missing conversions appear on time.
- C
Replace the connector with Snowpipe because Snowpipe automatically re-queries Google Analytics until source-system metrics stop changing.
- D
Create a materialized view on top of the target table because materialized views backfill missing source records when Google Analytics republishes historical metrics.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to reprocess a rolling lookback window of recent dates. Google Analytics data is known to be subject to latency and post-publication adjustment, so architects should design ingestion with source-system behavior in mind rather than assuming immutability after the first load. For the Snowflake Connector for Google Analytics, a maintainable pattern is to schedule recurring refreshes for recent periods so late-arriving or corrected metrics are captured automatically. This aligns with general Snowflake architecture best practices: handle mutable upstream data with controlled reprocessing logic, and do not rely on warehouse scaling, materialized views, or unrelated ingestion services such as Snowpipe to compensate for source-side data finalization behavior.
- A. Correct.
Correct. Google Analytics data can be delayed or adjusted after the initial extraction window, so a common architectural pattern is to reload a recent date range rather than assume each day is final immediately after first ingestion. A rolling lookback window improves completeness for mutable analytics data while preserving automation. This is the most practical design choice when using the Snowflake Connector for Google Analytics.
- B. Incorrect.
Incorrect. Warehouse size affects Snowflake-side compute performance, not when Google Analytics makes data complete or stable. If the source system publishes or finalizes data later, scaling Snowflake compute does not solve that source latency problem. This option reflects the misconception that all ingestion delays are caused by Snowflake processing bottlenecks.
- C. Incorrect.
Incorrect. Snowpipe is designed for continuous ingestion of files, typically from cloud storage, not for directly polling Google Analytics APIs and handling changing historical metrics. Replacing the connector with Snowpipe does not address the core issue and misapplies a Snowflake ingestion service to a use case it does not natively solve.
- D. Incorrect.
Incorrect. Materialized views optimize query performance for derived results from existing table data; they do not retrieve missing records from an external source or re-ingest corrected Google Analytics data. This option confuses downstream query acceleration with source data refresh and correction handling.