Google ACE Practice Question: A team needs to export all Cloud Logging entries…

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Schedule a daily Cloud Function to query the Logging API and write results to BigQuery

  Why it's wrong here

  A daily Cloud Function that queries the Logging API and writes to BigQuery introduces up to 24 hours of latency, meaning log data would be stale and unsuitable for near-real-time analytics or operational monitoring. Additionally, that approach requires you to manage pagination, error handling, idempotency, and schema evolution in custom code, whereas a log sink automatically handles these concerns and delivers log entries as they are generated. A one-time backfill or a special-case query might make sense, but as a standing solution it is far less reliable and more complex than using Log Router sinks.

• ✓

  Create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset

  Why this is correct

  A log sink configured in the Log Router provides a managed, continuous export path: every matching log entry is streamed to the specified BigQuery dataset in near-real-time, with no further manual steps or custom code required. Because the sink is evaluated at ingestion time, all future logs are automatically routed as they are written, including newly created log streams and resource types. This is the recommended and only fully managed way to achieve ongoing, low-latency Cloud Logging export to BigQuery.

• ✗

  Use BigQuery Data Transfer Service to pull logs from Cloud Logging on a schedule

  Why it's wrong here

  BigQuery Data Transfer Service is designed to move data from external SaaS sources such as Google Ads, Google Play, or Amazon S3, and it does not provide a built-in connector for pulling Cloud Logging logs. Cloud Logging has its own export mechanism (Log Router sinks), which is purpose-built for this task and operates continuously rather than on a scheduled batch basis. Even if you tried to schedule transfers via the API, you would need to write custom code to extract logs, which defeats the purpose of using the managed transfer service and would still not provide real-time ingestion.

• ✗

  Enable VPC Flow Logs and stream them directly to BigQuery

  Why it's wrong here

  VPC Flow Logs only capture metadata about network traffic—such as source/destination IP, ports, and bytes transferred—from VPC subnet interfaces, not the full range of Cloud Logging entries like application logs, auditing logs, or GAE logs. Moreover, VPC Flow Logs themselves generate log entries that must be exported through a Log Router sink; they do not 'stream directly' to BigQuery as a native destination. Enabling VPC Flow Logs would address only a narrow subset of logs and would not fulfill the requirement of routing all project logs to BigQuery.

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