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

Section	Objectives
Topic 1: Implement security and compliance	- Secure CI/CD pipelines 1. Secret and credential management 2. IAM and least privilege access - Compliance and governance 1. Audit logging and policy enforcement
Topic 2: Optimize performance and continuous delivery	- Monitoring and observability 1. Performance tuning and feedback loops 2. Cloud Monitoring and Logging - System performance optimization 1. Scaling strategies and load handling
Topic 3: Develop and implement CI/CD pipelines	- Build and manage CI/CD pipelines using Google Cloud tools 1. Artifact repository management 2. Cloud Build pipeline design - Automate build, test, and deployment processes 1. Release management practices 2. Deployment automation strategies
Topic 4: Implement site reliability engineering (SRE) practices	- Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies - Incident management and postmortems 1. Alerting and on-call practices

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
Your team is running microservices in Google Kubernetes Engine (GKE) You want to detect consumption of an error budget to protect customers and define release policies What should you do?

A. Create a SLO Create an Alert Policy on select_slo_bum_rate
B. Create a SLO and configure uptime checks for your services Enable Alert Policies if the services do not pass
C. Create SLIs from metrics Enable Alert Policies if the services do not pass
D. Use the metrics from Anthos Service Mesh to measure the health of the microservices

Answer: A

Explanation:
The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.

NEW QUESTION # 205
You are writing a postmortem for an incident that severely affected users. You want to prevent similar incidents in the future. Which two of the following sections should you include in the postmortem? (Choose two.)

A. A list of action items to prevent a recurrence of the incident
B. Copies of the design documents for all the services impacted by the incident
C. A list of employees responsible for causing the incident
D. Your opinion of the incident's severity compared to past incidents
E. An explanation of the root cause of the incident

Answer: A,E

Explanation:
Explanation
For a postmortem to be truly blameless, it must focus on identifying the contributing causes of the incident without indicting any individual or team for bad or inappropriate behavior.

NEW QUESTION # 206
You recently deployed your application in Google Kubernetes Engine (GKE) and now need to release a new version of the application You need the ability to instantly roll back to the previous version of the application in case there are issues with the new version Which deployment model should you use?

A. Perform a blue/green deployment and test your new application after the deployment is complete
B. Perform a canary deployment, and test your new application periodically after the new version is deployed
C. Perform A. B testing, and test your application periodically after the deployment is complete
D. Perform a rolling deployment and test your new application after the deployment is complete

Answer: A

Explanation:
Explanation
The best deployment model for releasing a new version of your application in GKE with the ability to instantly roll back to the previous version is to perform a blue/green deployment and test your new application after the deployment is complete. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can minimize downtime and risk during deployment.

NEW QUESTION # 207
You use Google Cloud Managed Service for Prometheus with managed collection to gather metrics from your service running on Google Kubernetes Engine (GKE). After deploying the service, there is no metric data appearing in Cloud Monitoring, and you have not encountered any error messages. You need to troubleshoot this issue. What should you do?

A. Confirm that your service has the monitoring.servicesViewer IAM role.
B. Verify that your PodMonitoring configuration references a valid port.
C. Determine if your service has exceeded its quota for writes to the Cloud Monitoring API.
D. Check if the Grafana service is installed on your GKE cluster.

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
When using Managed Service for Prometheus, metrics may not appear in Cloud Monitoring if PodMonitoring is misconfigured. The most likely issue is that the PodMonitoring configuration does not reference a valid port.
PodMonitoring is required to collect metrics from workloads in GKE.
If the port is incorrect or missing, metrics won't be scraped.
#Why not other options?
A (Check quota limits)## If quotas were exceeded, you'd see explicit errors, but the question states no errors were encountered.
B (Grafana installation check)## Grafana is not required for Prometheus metric collection.
C (monitoring.servicesViewer IAM role issue)## This role allows viewing metrics, but does not affect metric collection.
#Official Reference:
Managed Service for Prometheus - PodMonitoring
GKE Monitoring Troubleshooting

NEW QUESTION # 208
You manage an application that runs in Google Kubernetes Engine (GKE) and uses the blue/green deployment methodology Extracts of the Kubernetes manifests are shown below:

The Deployment app-green was updated to use the new version of the application During post-deployment monitoring you notice that the majority of user requests are failing You did not observe this behavior in the testing environment You need to mitigate the incident impact on users and enable the developers to troubleshoot the issue What should you do?

A. Change the selector on the Service app-svc to app: my-app, version: blue
B. Update the Deployment app-blue to use the new version of the application
C. Update the Deployment ape-green to use the previous version of the application
D. Change the selector on the Service app-2vc to app: my-app.

Answer: D

Explanation:
The best option for mitigating the incident impact on users and enabling the developers to troubleshoot the issue is to change the selector on the Service app-svc to app: my-app, version: blue. A Service is a resource that defines how to access a set of Pods. A selector is a field that specifies which Pods are selected by the Service. By changing the selector on the Service app-svc to app: my-app, version: blue, you can ensure that the Service only routes traffic to the Pods that have both labels app: my-app and version: blue. These Pods belong to the Deployment app-blue, which uses the previous version of the application. This way, you can mitigate the incident impact on users by switching back to the working version of the application. You can also enable the developers to troubleshoot the issue with the new version of the application in the Deployment app-green without affecting users.

NEW QUESTION # 209
......

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