The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).
Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests.Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.
Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster.This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.
Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources.You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.
By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices.You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.
References:
1:Overview | Artifact Registry Documentation | Google Cloud
2: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
3: Installing Config Connector | Config Connector Documentation | Google Cloud
4: Why use Config Connector? | Config Connector Documentation | Google Cloud

Comprehensive and Detailed Explanation:
Google Cloud Trace is specifically designed to analyze request latency and identify performance bottlenecks across services. Since the issue is related to slow performance after a new release, the best approach is to use Cloud Trace to:
Visualize request latency across services
Pinpoint slow dependencies affecting response time
Analyze performance trends over time
#Why not other options?
B (Error Reporting)## Focuses on uncaught exceptions and crashes, not latency issues.
C (Cloud Profiler)## Helps with CPU and memory analysis, not request tracing.
D (Prometheus for Cloud Monitoring)## Useful for metrics collection, but not ideal for debugging specific request latency issues.
#Official Reference:
Google Cloud Trace Overview

Comprehensive and Detailed Explanation:
Cloud Deploy is a GCP-native managed CI/CD tool for reliable and automated releases across environments.
Artifact Registry Cleanup Policy automates image retention and deletion of old images, reducing storage costs and avoiding clutter.
Rollback and rollback policies should be handled by Cloud Deploy, not manual interventions.
#Why not other options?
A## Cloud Build jobs for cleanup are not scalable or automated compared to Artifact Registry policies.
B## Cloud Service Mesh is not related to deployment rollbacks or image cleanup.
D## Cloud Deploy provides better rollback management than a custom rollback pipeline.
#Official Reference:
Google Cloud Deploy Overview
Artifact Registry Cleanup Policy