Free Access Google.Professional-Cloud-DevOps-Engineer.v2025-09-27.q141 Practice Test (Page 5)

Question 16

You need to define Service Level Objectives (SLOs) for a high-traffic multi-region web application. Customers expect the application to always be available and have fast response times. Customers are currently happy with the application performance and availability. Based on current measurement, you observe that the 90th percentile of latency is 120ms and the 95th percentile of latency is 275ms over a 28-day window. What latency SLO would you recommend to the team to publish?

A.90th percentile - 100ms
95th percentile - 250ms

B.90th percentile - 120ms
95th percentile - 275ms

C.90th percentile - 150ms
95th percentile - 300ms

D.90th percentile - 250ms
95th percentile - 400ms

Question 17

Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?

A.Publish various metrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.

B.Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.

C.Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.

D.Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.

Question 18

You support an e-commerce application that runs on a large Google Kubernetes Engine (GKE) cluster deployed on-premises and on Google Cloud Platform. The application consists of microservices that run in containers. You want to identify containers that are using the most CPU and memory. What should you do?

A.Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.

B.Use Stackdriver Logging to export application logs to BigOuery. aggregate logs per container, and then analyze CPU and memory consumption.

C.Use Stackdriver Kubernetes Engine Monitoring.

D.Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.

Question 19

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?

A.Increase the Pod CPU and memory limits

B.Decrease the acknowledgment deadline on the subscription

C.Add a virtual queue to the online store that allows typical traffic levels

D.Increase the number of Pod replicas

Question 20

You are implementing a CI'CD pipeline for your application in your company s multi-cloud environment Your application is deployed by using custom Compute Engine images and the equivalent in other cloud providers You need to implement a solution that will enable you to build and deploy the images to your current environment and is adaptable to future changes Which solution stack should you use'?

A.Cloud Build with Packer

B.Cloud Build with Google Cloud Deploy

C.Google Kubernetes Engine with Google Cloud Deploy

D.Cloud Build with kpt

Correct Answer: B

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.

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