PDE Maintaining and Automating Data Workloads Practice Question
A data engineering team manages BigQuery datasets across multiple projects. They want to automatically detect and respond when a scheduled query fails, and they want the response to create an incident in their existing ticketing system. The team prefers minimal custom infrastructure and wants to use native Google Cloud tooling. Which approach should they use?
⚠ Common exam trap
The trap here is choosing polling-based approaches, which add latency and maintenance, instead of event-driven log-based alerting that natively captures BigQuery failures.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable BigQuery audit logs, create a log-based alerting policy in Cloud Monitoring, and route the alert to a Pub/Sub topic that triggers a Cloud Function to open the ticket.
Cloud Audit Logs capture BigQuery job failures, and log-based alerting policies in Cloud Monitoring can trigger Pub/Sub notifications. A Cloud Function subscribed to the topic can call the ticketing API, creating incidents automatically. This event-driven chain uses managed services, avoids polling, and requires little custom code compared with the other options.
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 Cloud Scheduler job that polls the BigQuery INFORMATION_SCHEMA.JOBS view and emails the team when errors are found.
Why it's wrong here
Polling INFORMATION_SCHEMA.JOBS introduces latency and requires custom parsing logic, and email is not integration with the ticketing system. It also may miss failures between polling intervals. This approach needs more custom infrastructure than log-based alerting and does not directly create incidents.
- ✗
Configure BigQuery to send failure notifications to a Cloud Storage bucket and use a Dataproc job to parse them.
Why it's wrong here
BigQuery does not natively write job failure notifications to Cloud Storage. This would require custom export configuration and a Dataproc cluster to parse files, adding significant infrastructure and cost. It does not use the simplest native path and adds unnecessary processing components.
- ✓
Enable BigQuery audit logs, create a log-based alerting policy in Cloud Monitoring, and route the alert to a Pub/Sub topic that triggers a Cloud Function to open the ticket.
Why this is correct
BigQuery writes job failure information to Cloud Audit Logs. A log-based alerting policy in Cloud Monitoring can match failed query jobs and publish to a Pub/Sub topic, which invokes a Cloud Function that calls the ticketing API. This uses native tooling with minimal custom infrastructure and reliably captures scheduled query failures.
- ✗
Use the BigQuery REST API from a long-running Compute Engine VM to poll for failed jobs and call the ticketing API.
Why it's wrong here
A long-running VM polling the API is custom infrastructure that must be maintained and scaled. It duplicates functionality already provided by Cloud Audit Logs and Cloud Monitoring. This approach increases operational overhead and does not leverage native event-driven alerting, contrary to the team's stated preference.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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