Google PCA Ensure solution and operations reliability Practice Question
A logistics company runs a Cloud Run service that processes shipment events. They want to be notified and to trigger an automated rollback when the error rate of a new revision exceeds a threshold shortly after deployment. Which Google Cloud feature should they use?
⚠ Common exam trap
The trap here is assuming Cloud Run has a native error-rate-based automatic rollback, when automated rollback is orchestrated through Cloud Deploy verification instead.
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
✓
Create a Cloud Monitoring alerting policy on the Cloud Run error rate and use Cloud Deploy with a deployment verification and automated rollback.
Cloud Deploy deployment verification evaluates a new Cloud Run revision against defined criteria, such as a Cloud Monitoring alert on error rate, and automatically rolls back to the prior stable release when verification fails. Pairing it with a Cloud Monitoring alerting policy delivers both the notification and the automated rollback the logistics team requires.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Error Reporting to group exceptions and configure a Pub/Sub notification that emails the on-call engineer to perform a rollback.
Why it's wrong here
Error Reporting aggregates and groups application errors and can notify via Pub/Sub, which helps visibility. However, it still requires a human to execute the rollback, so recovery is not automated. The scenario explicitly wants an automated rollback when the threshold is exceeded, making notification-only approaches insufficient for the requirement.
- ✗
Enable Cloud Run's built-in automatic rollback by setting a maximum error rate in the service YAML.
Why it's wrong here
Cloud Run does not provide a built-in automatic rollback triggered by an error-rate threshold in the service configuration. While Cloud Run supports traffic splitting and health checks for startup, it does not autonomously revert a revision based on runtime error rates. Relying on a nonexistent feature would leave the bad revision serving traffic, so this option is incorrect.
- ✗
Configure Cloud Run gradual rollout with canary traffic splitting and manually monitor the revision before shifting all traffic.
Why it's wrong here
Gradual rollout and traffic splitting limit the blast radius of a bad revision, which is valuable, but they require a human to watch metrics and decide to shift traffic back. The scenario asks for notification and an automated rollback when the error rate exceeds a threshold, so a manual canary process does not meet the automation requirement on its own.
- ✓
Create a Cloud Monitoring alerting policy on the Cloud Run error rate and use Cloud Deploy with a deployment verification and automated rollback.
Why this is correct
Cloud Deploy supports deployment strategies with verification, where a Cloud Monitoring alert or custom job evaluates the new revision and, on failure, automatically rolls back to the previous stable release. This provides both the notification via the alerting policy and the automated rollback the team wants, without manual intervention, matching the stated requirement precisely.
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IAM Policies, Service Accounts, and Auditing
Key term
Cloud Run
Cloud Run is a fully managed compute platform from Google Cloud that lets you run containerized applications in a serverless environment, automatically scaling from zero to thousands of requests.
Key term
Cloud Monitoring
Cloud monitoring is the process of observing, measuring, and managing an organization's cloud infrastructure and applications to ensure performance, availability, and security.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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