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

Cloud Build is a fully managed continuous integration and continuous delivery (CI/CD) service that helps you automate your builds, tests, and deployments. Google Cloud Deploy is a service that automates the deployment of your applications to Google Kubernetes Engine (GKE).

Together, Cloud Build and Google Cloud Deploy can be used to build and deploy your application's custom Compute Engine images to your current environment and to other cloud providers in the future.

Here are the steps involved in using Cloud Build and Google Cloud Deploy to implement a CI/CD pipeline for your application:

Create a Cloud Build trigger that fires whenever a change is made to your application's code.

In the Cloud Build trigger, configure Cloud Build to build your application's Docker image.

Create a Google Cloud Deploy configuration file that specifies how to deploy your application's Docker image to GKE.

In Google Cloud Deploy, create a deployment that uses your configuration file.

Once you have created the Cloud Build trigger and Google Cloud Deploy configuration file, any changes made to your application's code will trigger Cloud Build to build a new Docker image. Google Cloud Deploy will then deploy the new Docker image to GKE.

This solution stack is adaptable to future changes because it uses a cloud-agnostic approach. Cloud Build can be used to build Docker images for any cloud provider, and Google Cloud Deploy can be used to deploy Docker images to any Kubernetes cluster.

The other solution stacks are not as adaptable to future changes. For example, solution stack A (Cloud Build with Packer) is limited to building Docker images for Compute Engine. Solution stack C (Google Kubernetes Engine with Google Cloud Deploy) is limited to deploying Docker images to GKE. Solution stack D (Cloud Build with kpt) is a newer solution that is not yet as mature as Cloud Build and Google Cloud Deploy.

Overall, the best solution stack for implementing a CI/CD pipeline for your application in a multi-cloud environment is Cloud Build with Google Cloud Deploy. This solution stack is fully managed, cloud-agnostic, and adaptable to future changes.

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

Google Cloud’s recommended practice for scalable, multi-cluster monitoring is Google Cloud Managed Service for Prometheus (GMP). This service is a fully managed, multi-cloud-capable solution built on top of Monarch (the same globally scalable time-series database Google uses internally). By using managed collection, GKE clusters automatically deploy collectors that scrape metrics without the operational overhead of managing a manual Prometheus server, scaling, or long-term storage (sharding).

This solution satisfies the "centralized and scalable" requirement because GMP allows you to query data across multiple projects and clusters using PromQL, which provides the flexible query language requested. While the Ops Agent (Option C) is useful for VM-based workloads, GMP is the purpose-built solution for Kubernetes environments. Standard sidecar deployments (Option A) or self-managed admin clusters (Option D) introduce significant administrative toil and fail to leverage the global scale and high availability of the Cloud Monitoring backend. By centralizing metrics in Metrics Explorer, SRE teams gain a unified view of system health, making it easier to define SLOs and manage incidents across a complex microservices landscape.

Comprehensive and Detailed Explanation From General Google Cloud Knowledge:

App Hub allows you to organize and discover services and applications within your Google Cloud environment. For App Hub to recognize and display resources as components of an "application," these resources often need to be explicitly registered or discovered as "services" within that application's configuration. While App Hub can automatically discover some resources (like GKE workloads, Cloud Run services), for other resources, or to establish specific relationships, manual registration or more detailed configuration is sometimes required.

Option A (Attach the project containing the forwarding rule as an App Hub service project): While App Hub works across projects (host project for the application, service projects for services and workloads), simply attaching the project might not be sufficient for App Hub to automatically pick up and categorize every resource like a forwarding rule specifically for a defined application without further context. The forwarding rule needs to be associated with a service within the App Hub application.

Option B (Enable the App Hub API in the project containing the forwarding rule): The App Hub API needs to be enabled in projects where you want to manage App Hub resources (applications, services, workloads). If it wasn't enabled, you likely wouldn't be able to see any resources from that project. Since other resources are visible, this is less likely the root cause for a single missing resource, though it's a prerequisite for App Hub to function at all with that project.

Option C (Configure the forwarding rule to forward to the correct target proxy): While correct configuration of the forwarding rule is essential for its operational functionality, App Hub's ability to list the forwarding rule is more about its discovery and registration within App Hub's model rather than its traffic-directing correctness. An incorrectly configured forwarding rule that is properly registered might still appear in App Hub, perhaps with an error status.

Option D (Register the forwarding rule as a service in the application configuration): App Hub applications are composed of "services," and these services are in turn composed of "workloads" or other discovered/registered resources. A forwarding rule is typically an entry point or part of the infrastructure for a service. Explicitly registering it or the resource it points to (which then allows App Hub to trace back to the forwarding rule) as a service or part of a service within the application configuration would make it visible and properly cataloged by App Hub. App Hub discovers resources by looking for specific labels or by manual registration. If it's not automatically discovered as part of a recognized workload (like a GCE instance group service exposed via a load balancer), explicit registration is often the way to make it appear.

Reference (Based on general App Hub functionality):

App Hub discovers resources that are part of registered applications and their services. Services in App Hub can be based on various Google Cloud resources. If a resource like a forwarding rule isn't automatically linked to a displayed workload, it might need to be explicitly defined as a service or part of a service.

From the Google Cloud documentation on App Hub concepts:

"Applications are the core organizational unit in App Hub. An application represents a logical system that delivers business value... Services represent the logical components of an application... Workloads are instances of your services running on Google Cloud infrastructure. App Hub automatically discovers workloads for supported resource types or you can manually register them."

Forwarding rules are associated with load balancing, which exposes services. If the service that the forwarding rule points to is correctly registered and identified by App Hub, associated infrastructure like the forwarding rule should typically be discoverable. If it's not, ensuring the service it fronts is correctly registered and that App Hub understands this link is key. Option D aligns with this concept of ensuring the relevant component (which the forwarding rule is part of) is registered within the application structure.

You can find more information in the official Google Cloud documentation regarding App Hub:

App Hub overview: https://cloud.google.com/app-hub/docs/overview

Registering services and workloads: Documentation would detail how different resources are discovered or need to be registered.

The correct answer is B. Use shadow testing with continuous deployment.

Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.

Comprehensive and Detailed Explanation From General Cloud Monitoring Knowledge:

The key requirement is to distinguish between failures in your own service and those caused by an underlying Google Cloud service.

A. Enable Personalized Service Health annotations on the dashboard: Google Cloud Personalized Service Health provides information about incidents affecting Google Cloud services that may impact your projects. When enabled and integrated with Monitoring, it can display these events as annotations on your dashboards, overlaying them on your service's metrics charts. This allows you to correlate dips in your service's performance with known Google Cloud service issues, directly addressing the need to distinguish failure origins.

B. Create an alerting policy for the system error metrics: Alerting policies are for notifications when metrics cross thresholds. While useful for detecting issues in your own service, they don't inherently distinguish the cause between your service and a Google Cloud dependency without further context, which option A provides.

C. Create a log-based metric to track cloud service errors, and display the metric on the dashboard: You could try to create log-based metrics from logs that might indicate a cloud service error (e.g., specific API error codes from Google Cloud services). However, this is indirect, might require complex parsing, and Personalized Service Health is a more direct and authoritative source for Google Cloud service disruptions.

D. Create a logs widget to display system errors from Cloud Logging on the dashboard: Similar to C, displaying raw system error logs can be helpful for troubleshooting your own service, but it doesn't provide a clear, curated view of whether a Google Cloud service itself is having an issue. It would require manual interpretation to link these logs to a potential Google Cloud outage.

Personalized Service Health is specifically designed to provide visibility into Google Cloud service incidents relevant to your resources. Integrating this with Monitoring dashboards is the most direct way to achieve the stated goal.

Reference (Based on Cloud Monitoring and Personalized Service Health features):

Personalized Service Health Overview: https://cloud.google.com/service-health/docs/overview

Integrating with Cloud Monitoring: Documentation often shows how to enable annotations for Personalized Service Health events on Monitoring charts. This allows a visual correlation between your service metrics and Google Cloud service health events."Personalized Service Health integrates with Cloud Monitoring so you can see service health events alongside your metrics."

"You can enable annotations on your metric charts to display relevant Personalized Service Health events."

This feature directly helps differentiate between issues in your application versus issues in the underlying Google Cloud services.

Comprehensive and Detailed Explanation:

The best approach to a joint-ownership DevOps model follows SRE and DevOps best practices, where:

The DevOps team manages infrastructure and is on-call for incidents.

The Software Development team owns SLOs and submits code.

Code reviews should be a shared responsibility to ensure both teams maintain quality.

📌Why not other options?

A (DevOps team reviewing all code)❌→ Breaks DevOps best practices by making developers dependent on the DevOps team for code review.

B (DevOps does code reviews, developers handle incidents)❌→ Developers should not handle on-call duties unless part of a shared rotation.

C (Developers manage infrastructure)❌→ Developers should not be fully responsible for infrastructure.

📌Official Reference:

SRE & DevOps Ownership Model

Shared Responsibility Model in Google Cloud

https://cloud.google.com/container-registry/docs/pushing-and-pulling

The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver andcreate a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.