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

Section	Weight	Objectives
Bootstrapping and maintaining a Google Cloud organization	20%	- Design resource hierarchy 1. Organize resources by project/folder/application 2. Plan shared networking and connectivity 3. Define IAM policies and access control - Manage configuration and deployment 1. Use Infrastructure as Code tools 2. Enforce consistency across environments 3. Automate resource provisioning - Set up governance and compliance 1. Manage resource quotas and limits 2. Configure audit logging and monitoring 3. Implement organization policies
Implementing observability and troubleshooting	20%	- Build monitoring and logging systems 1. Collect and analyze metrics, logs, traces 2. Create dashboards and alerts 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace - Troubleshoot complex issues 1. Analyze performance and latency 2. Trace requests end-to-end 3. Isolate problems across layers - Optimize observability strategy 1. Integrate observability into development 2. Reduce noise and prioritize signals 3. Scale monitoring for large environments
Building and implementing CI/CD pipelines	25%	- Design CI/CD pipelines 1. Plan pipelines for apps, infrastructure, ML workloads 2. Integrate artifact management 3. Support hybrid/multi-cloud deployment - Optimize and maintain pipelines 1. Monitor pipeline performance 2. Handle rollbacks and versioning 3. Improve speed and reliability - Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Manage pipeline secrets and credentials 3. Enforce security scanning and compliance checks
Applying site reliability engineering practices	18%	- Design for resilience 1. Plan redundancy and fault tolerance 2. Test failure scenarios and recovery 3. Automate remediation and self-healing - Define reliability goals 1. Use error budgets to balance change and stability 2. Align reliability with business needs 3. Establish SLIs, SLOs, SLAs - Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Implement improvements to prevent recurrence 3. Detect and respond to issues quickly
Optimizing performance and cost	17%	- Apply FinOps practices 1. Monitor and analyze cloud costs 2. Right-size resources and eliminate waste 3. Use spot instances and committed use discounts - Balance cost, performance, and reliability 1. Optimize over time 2. Plan capacity and scaling 3. Make tradeoff decisions based on data - Improve system performance 1. Use caching and content delivery 2. Optimize resource allocation 3. Tune applications and infrastructure

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
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?

A. Run the gcloud container clusters update logging=WORKLOAD command for the development cluster.
B. Run the gcloud container clusters update --logging-SYSTEM command for the development cluster.
C. Run the gcloud logging sinks update _Defau1t --disabled command in the project associated with the development environment.
D. Add the severity >= DEBUG resource. type "k83 container" exclusion filter to the Default logging sink in the project associated with the development environment.

Answer: B

NEW QUESTION # 80
You are the Operations Lead for an ongoing incident with one of your services. The service usually runs at around 70% capacity. You notice that one node is returning 5xx errors for all requests. There has also been a noticeable increase in support cases from customers. You need to remove the offending node from the load balancer pool so that you can isolate and investigate the node. You want to follow Google-recommended practices to manage the incident and reduce the impact on users. What should you do?

A. 1 . Drain traffic from the unhealthy node and remove the old node from service.2. Add a new node to the pool, wait for the new node to report as healthy, and then serve traffic to the new node.3. Monitor traffic to ensure that the pool is healthy and is handling traffic appropriately.4. Communicate your actions to the incident team.
B. 1. Communicate your intent to the incident team.2. Add a new node to the pool, and wait for the new node to report as healthy.3. When traffic is being served on the new node, drain traffic from the unhealthy node, and remove the old node from service.
C. 1 . Drain traffic from the unhealthy node and remove the node from service.2. Monitor traffic to ensure that the error is resolved and that the other nodes in the pool are handling the traffic appropriately.3.
Scale the pool as necessary to handle the new load.4. Communicate your actions to the incident team.
D. 1. Communicate your intent to the incident team.2. Perform a load analysis to determine if the remaining nodes can handle the increase in traffic offloaded from the removed node, and scale appropriately.3. When any new nodes report healthy, drain traffic from the unhealthy node, and remove the unhealthy node from service.

Answer: D

Explanation:
The correct answer is A. Communicate your intent to the incident team. Perform a load analysis to determine if the remaining nodes can handle the increase in traffic offloaded from the removed node, and scale appropriately. When any new nodes report healthy, drain traffic from the unhealthy node, and remove the unhealthy node from service.
This answer follows the Google-recommended practices for incident management, as described in the Chapter
9 - Incident Response, Google SRE Book1. According to this source, some of the best practices are:
Maintain a clear line of command. Designate clearly defined roles. Keep a working record of debugging and mitigation as you go. Declare incidents early and often.
Communicate your intent before taking any action that might affect the service or the incident response. This helps to avoid confusion, duplication of work, or unintended consequences.
Perform a load analysis before removing a node from the load balancer pool, as this might affect the capacity and performance of the service. Scale the pool as necessary to handle the expected load.
Drain traffic from the unhealthy node before removing it from service, as this helps to avoid dropping requests or causing errors for users.
Answer A follows these best practices by communicating the intent to the incident team, performing a load analysis and scaling the pool, and draining traffic from the unhealthy node before removing it.
Answer B does not follow the best practice of performing a load analysis before adding or removing nodes, as this might cause overloading or underutilization of resources.
Answer C does not follow the best practice of communicating the intent before taking any action, as this might cause confusion or conflict with other responders.
Answer D does not follow the best practice of draining traffic from the unhealthy node before removing it, as this might cause errors for users.
References:
1:Chapter 9 - Incident Response, Google SRE Book

NEW QUESTION # 81
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?

A. Delete the new container image, and delete the running Pods
B. Run the kubectl rollout undo command
C. Scale the new Kubernetes Deployment to zero
D. Update the Kubernetes Service to point to the previous Kubernetes Deployment

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 # 82
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 and configure uptime checks for your services Enable Alert Policies if the services do not pass
B. Use the metrics from Anthos Service Mesh to measure the health of the microservices
C. Create a SLO Create an Alert Policy on select_slo_bum_rate
D. Create SLIs from metrics Enable Alert Policies if the services do not pass

Answer: C

Explanation:
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 # 83
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_dataset You need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

A. Create an aggregated Iog sink in the Dev and Prod folders
B. Create a log sink in each project
C. Create two aggregated log sinks at the organization level, and filter by project ID
D. Create a single aggregated log sink at the organization level.

Answer: A

Explanation:
Explanation
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export.
Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

NEW QUESTION # 84
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