Question 1

Your company runs services by using Google Kubernetes Engine (GKE). The GKE clusters in the development environment run applications with verbose logging enabled. Developers view logs by using the kubect1 logs

command and do not use Cloud Logging. Applications do not have a uniform logging structure defined. You need to minimize the costs associated with application logging while still collecting GKE operational logs. What should you do?

ARun the gcloud container clusters update --logging---SYSTEM command for the development cluster.

BRun the gcloud container clusters update logging=WORKLOAD command for the development cluster.

CRun the gcloud logging sinks update _Defau1t --disabled command in the project associated with the development environment.

DAdd the severity >= DEBUG resource. type 'k83 container' exclusion filter to the Default logging sink in the project associated with the development environment.

Answer : A

Question 2

You are the Site Reliability Engineer responsible for managing your company's data services and products. You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?

ADeploy the prototype product in a test environment, run a load test, and share the results with the product development team.

BWhen the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performance metrics with the product development team.

CWhen the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.

DReview the design of the product with the product development team to provide feedback early in the design phase.

Answer : D

The correct answer is D, Review the design of the product with the product development team to provide feedback early in the design phase.

According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.

Preparing for Google Cloud Certification: Cloud DevOps Engineer Professional Certificate, Course 1: Site Reliability Engineering and DevOps, Week 1: Introduction to SRE and DevOps.

Question 3

You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Of your applications. You need to test against the full production load before your applications are launched. What should you do?

AUse A/B testing with blue/green deployment.

BUse shadow testing with continuous deployment.

CUse canary testing with continuous deployment.

DUse canary testing with rolling updates deployment,

Answer : B

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.

Application deployment and testing strategies, Testing strategies, Shadow test pattern.

Question 4

As a Site Reliability Engineer, you support an application written in GO that runs on Google Kubernetes Engine (GKE) in production. After releasing a new version Of the application, you notice the application runs for about 15 minutes and then restarts. You decide to add Cloud Profiler to your application and now notice that the heap usage grows constantly until the application restarts. What should you do?

AAdd high memory compute nodes to the cluster.

BIncrease the memory limit in the application deployment.

CAdd Cloud Trace to the application, and redeploy.

DIncrease the CPU limit in the application deployment.

Answer : B

The correct answer is B, Increase the memory limit in the application deployment.

The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory. The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

Using Cloud Profiler with Go, Troubleshooting memory leaks. Profiling Go Programs, Heap profiles.

Question 5

You need to create a Cloud Monitoring SLO for a service that will be published soon. You want to verify that requests to the service will be addressed in fewer than 300 ms at least 90% Of the time per calendar month. You need to identify the metric and evaluation method to use. What should you do?

ASelect a latency metric for a request-based method of evaluation.

BSelect a latency metric for a window-based method of evaluation.

CSelect an availability metric for a request-based method of evaluation.

DSelect an availability metric for a window-based method Of evaluation.

Answer : A

The correct answer is

A, Select a latency metric for a request-based method of evaluation.

A latency metric measures how responsive your service is to users. For example, you can use the cloud.googleapis.com/http/server/response_latencies metric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests. For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month. You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.

Creating an SLO | Operations Suite | Google Cloud, Choosing a metric, Latency metric. Concepts in service monitoring | Operations Suite | Google Cloud, Service-level indicators, Request-based SLIs. Learn how to set SLOs -- SRE tips | Google Cloud Blog, Setting SLOs.

Free Professional Cloud DevOps Engineer Questions for Google Professional Cloud DevOps Engineer Exam as PDF & Practice Test Software

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