Professional-Cloud-DevOps-Engineer Exam Dumps : Google Cloud Certified - Professional Cloud DevOps Engineer Exam

 The best option for implementing guard rails on all GKE clusters to only allow the deployment of trusted and approved images is to use Binary Authorization to attest images during your CI/CD pipeline. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization to create policies that specify which images are allowed or denied in your GKE clusters. You can also use Binary Authorization to attest images during your CI/CD pipeline by using tools such as Container Analysis or third-party integrations. An attestation is a digital signature that certifies that an image meets certain criteria, such as passing vulnerability scans or code reviews. By using Binary Authorization to attest images during your CI/CD pipeline, you can ensure that only trusted and approved images are deployed to your GKE clusters.

Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:

In incident management, the primary goal during the "Mitigation" phase is to restore service levels as quickly as possible, often referred to as "stopping the bleeding." Google Cloud’s SRE principles emphasize that during a high-priority incident, you should prioritize actions that have an immediate impact over long-term fixes or deep-dive forensics. While a rollback (Option C) is a standard procedure, the 15-minute CI/CD overhead is too slow when a critical payment service is failing. Using Cloud Profiler (Option A) is a diagnostic step that should be reserved for the post-mortem phase, not the heat of the outage.

Canceling the active Dataflow job (Option D) is the fastest way to alleviate the pressure on the shared Memorystore instance. Since the Dataflow pipeline is "non-critical" and "batch-based," stopping it has a low business impact compared to the payment service outage. Once the job is canceled, the write pressure on Redis drops instantly, reducing read latency and restoring the payment service to health. This aligns with the SRE practice of "shedding load" to protect critical system components. Once the immediate crisis is resolved, the team can then investigate the root cause and implement long-term fixes like rate limiting or resource isolation.