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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	Implementing service monitoring strategies: This topic covers structured and unstructured logs from Compute Engine, GKE, and serverless platforms using Cloud Logging. Its sub-topics include coverage of metrics with Cloud Monitoring, dashboards and alerts in Cloud Monitoring, Cloud Logging platform, and logging and monitoring access controls.
Topic 2	Bootstrapping a Google Cloud organization for DevOps: It discusses the overall resource hierarchy for an organization, infrastructure as code, CI CD architecture stack in Google Cloud, hybrid, and multi-cloud environments. Moreover, the topic focuses on multiple environments including staging and production.
Topic 3	Building and implementing CI CD pipelines for a service: Management and designing of CI CD pipelines, implementation of CI CD pipelines, management of CI CD configuration, and secrets are discussed in this paper. Moreover, it focuses on storage methods and key rotation services, and finally securing the CI CD deployment pipeline.
Topic 4	Applying site reliability engineering practices to a service: It deals with velocity, and reliability of the service, service lifecycle, healthy communication, and collaboration for operations. The topic also discusses mitigation of incident impact on users and conducting a postmortem.
Topic 5	Optimizing the service performance: It delves into the identification of service performance issues, implementation of debugging tools in Google Cloud, and optimization of resource utilization and costs.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
You support a high-traffic web application and want to ensure that the home page loads in a timely manner. As a first step, you decide to implement a Service Level Indicator (SLI) to represent home page request latency with an acceptable page load time set to 100 ms. What is the Google-recommended way of calculating this SLI?

A. Count the number of home page requests that load in under 100 ms, and then divide by the total number of home page requests.
B. Count the number of home page requests that load in under 100 ms. and then divide by the total number of all web application requests.
C. Buckelize Ihe request latencies into ranges, and then compute the percentile at 100 ms.
D. Bucketize the request latencies into ranges, and then compute the median and 90th percentiles.

Answer: A

NEW QUESTION # 77
You support a production service that runs on a single Compute Engine instance. You regularly need to spend time on recreating the service by deleting the crashing instance and creating a new instance based on the relevant image. You want to reduce the time spent performing manual operations while following Site Reliability Engineering principles. What should you do?

A. Create a Stackdriver Monitoring dashboard with SMS alerts to be able to start recreating the crashed instance promptly after it has crashed.
B. Create a Managed Instance Group with a single instance and use health checks to determine the system status.
C. File a bug with the development team so they can find the root cause of the crashing instance.
D. Add a Load Balancer in front of the Compute Engine instance and use health checks to determine the system status.

Answer: C

NEW QUESTION # 78
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. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
B. 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.
C. Publish various metrics from the application directly to the Slackdriver Monitoring API, and then observe these custom 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.

Answer: C

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#custom_metrics
https://github.com/GoogleCloudPlatform/k8s-stackdriver/blob/master/custom-metrics-stackdriver-adapter
/README.md
Your application can report a custom metric to Cloud Monitoring. You can configure Kubernetes to respond to these metrics and scale your workload automatically. For example, you can scale your application based on metrics such as queries per second, writes per second, network performance, latency when communicating with a different application, or other metrics that make sense for your workload.https://cloud.google.com
/kubernetes-engine/docs/concepts/custom-and-external-metrics

NEW QUESTION # 79
Your product is currently deployed in three Google Cloud Platform (GCP) zones with your users divided between the zones. You can fail over from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk:
* Mean Time to Detect (MUD} in minutes
* Mean Time to Repair (MTTR) in minutes
* Mean Time Between Failure (MTBF) in days
* User Impact Percentage
The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?

A. MTTD: 5
MTTR: 10
MTBF: 90
Impact: 33%
B. MTTD:5
MTTR: 20
MTBF: 90
Impact: 50%
C. MTTD:5
MTTR: 20
MTBF: 90
Impact: 33%
D. MTTD:5
MTTR: 10
MTBF: 90
Impact 50%

Answer: C

Explanation:
Explanation
https://www.atlassian.com/incident-management/kpis/common-metrics
https://linkedin.github.io/school-of-sre/

NEW QUESTION # 80
Your applications performance in Google Cloud has degraded since the last release You suspect that downstream dependencies might be causing some requests to take longer to complete You need to investigate the issue with your application to determine the cause What should you do?

A. Configure Cloud Profiler in your application
B. Configure Cloud Trace in your application
C. Configure Google Cloud Managed Service for Prometheus in your application
D. Configure Error Reporting in your application

Answer: B

Explanation:
The best option for investigating the issue with your application's performance in Google Cloud is to configure Cloud Trace in your application. Cloud Trace is a service that allows you to collect and analyze latency data from your application. You can use Cloud Trace to trace requests across different components of your application, such as downstream dependencies, and identify where they take longer to complete. You can also use Cloud Trace to compare latency data across different versions of your application, and detect any performance degradation or improvement. By using Cloud Trace, you can diagnose and troubleshoot performance issues with your application in Google Cloud.

NEW QUESTION # 81
......

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