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Title: Reliable Google Professional-Cloud-DevOps-Engineer Test Sims - Dump Professional
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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q128-Q133):NEW QUESTION # 128
You manage a critical, user-facing application and have configured a service level objective (SLO) in Cloud Monitoring to track 99% availability over a 30-day rolling window. Recently, a series of minor issues have increased latency, causing the error budget to be consumed at an accelerated rate. You need to be proactively notified when the service is at risk of violating its SLO before the error budget is fully depleted. What should you do?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
In Site Reliability Engineering (SRE), the most effective way to manage an SLO is through Burn Rate Alerting. According to Google Cloud's SRE documentation, a burn rate is the speed at which the error budget is being consumed relative to the SLO's compliance period. Relying on simple threshold alerts (Option D) or manual dashboard checks (Option B) often leads to "alert fatigue" or missed signals because they do not account for the rate of depletion over time.
By creating an alerting policy in Cloud Monitoring specifically based on the burn rate, you can identify
"fast burns" (sudden outages) and "slow burns" (gradual regressions like increased latency). The system calculates the projection; if the current rate of error budget consumption is high enough to exhaust the remaining budget before the end of the 30-day window, it triggers a proactive notification. This allows the team to intervene while they still have a portion of the error budget remaining, effectively preventing an SLO violation rather than merely reacting to one after the fact. This approach aligns perfectly with Google Cloud's recommended practices for automated, data-driven incident prevention.

NEW QUESTION # 129
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?
Answer: B
Explanation:
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

NEW QUESTION # 130
You are building the Cl/CD pipeline for an application deployed to Google Kubernetes Engine (GKE) The application is deployed by using a Kubernetes Deployment, Service, and Ingress The application team asked you to deploy the application by using the blue'green deployment methodology You need to implement the rollback actions What should you do?
Answer: D
Explanation:
The best option for implementing the rollback actions is to update the Kubernetes Service to point to the previous Kubernetes Deployment. A Kubernetes Service is a resource that defines how to access a set of Pods. A Kubernetes Deployment is a resource that manages the creation and update of Pods. By using the blue/green deployment methodology, you can create two Deployments, one for the current version (blue) and one for the new version (green), and use a Service to switch traffic between them. If you need to rollback, you can update the Service to point to the previous Deployment (blue) and stop sending traffic to the new Deployment (green).

NEW QUESTION # 131
You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?
Answer: D
Explanation:
Explanation
The correct answer is B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift. You can also use Terraform workspaces to isolate the state files for each environment12.
For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments. You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch. You can also use tags or labels to trigger deployments to different environments5 .
References:
1: Best practices for using Terraform | Google Cloud
2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn
3: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud

NEW QUESTION # 132
You encounter a large number of outages in the production systems you support. You receive alerts for all the outages that wake you up at night. The alerts are due to unhealthy systems that are automatically restarted within a minute. You want to set up a process that would prevent staff burnout while following Site Reliability Engineering practices. What should you do?
Answer: D
Explanation:
Explanation
Eliminate bad monitoring : Unactionable alerts (i.e., spam)
https://cloud.google.com/blog/pr ... with-sre-principles agree with kyubiblaze about having to remove unactionable items aka spam: "good monitoring alerts on actionable problems" @
https://cloud.google.com/blog/pr ... with-sre-principles

NEW QUESTION # 133
......
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