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PROFESSIONAL-DATA-ENGINEER · Question #5
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in…
The correct answer is B. Retry the query with exponential backoff, up to a cap of 15 minutes. Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure? Explanation/Reference…
Submitted by stefanr· Mar 30, 2026Building and operationalizing data processing systems
Question
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?
Options
- AIssue a command to restart the database servers.
- BRetry the query with exponential backoff, up to a cap of 15 minutes.
- CRetry the query every second until it comes back online to minimize staleness of data.
- DReduce the query frequency to once every hour until the database comes back online.
How the community answered
(19 responses)- A5% (1)
- B79% (15)
- C11% (2)
- D5% (1)
Explanation
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure? Explanation/Reference: https://cloud.google.com/sql/docs/mysql/manage-connections
Topics
#exponential backoff#error handling#data ingestion pipeline#resilience
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