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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
You are running a web application deployed to a Compute Engine managed instance group Ops Agent is installed on all instances You recently noticed suspicious activity from a specific IP address You need to configure Cloud Monitoring to view the number of requests from that specific IP address with minimal operational overhead. What should you do?

A. Update the application to export the IP address request metrics to the Cloud Monitoring API
B. Create a script to scrape the web server log Export the IP address request metrics to the Cloud Monitoring API
C. Configure the Ops Agent with a logging receiver Create a logs-based metric
D. Configure the Ops Agent with a metrics receiver

Answer: C

Explanation:
The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver and create a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.

NEW QUESTION # 49
The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your pre-production environment and you need to ensure that the application does not have performance problems after deployment Your deployment must be automated What should you do?

A. Deploy the application through a continuous delivery pipeline by using canary deployments Use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics
B. Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues
C. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues
D. Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.

Answer: A

Explanation:
The best option for deploying a new version of your containerized application to production on GKE and ensuring that the application does not have performance problems after deployment is to deploy the application through a continuous delivery pipeline by using canary deployments, use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A canary deployment is a deployment strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test the new version in the production environment with real traffic and load, and gradually increase the traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can also use Cloud Monitoring to set up alerts and dashboards to track the performance of your application.

NEW QUESTION # 50
You are performing a semi-annual capacity planning exercise for your flagship service You expect a service user growth rate of 10% month-over-month for the next six months Your service is fully containerized and runs on a Google Kubemetes Engine (GKE) standard cluster across three zones with cluster autoscaling enabled You currently consume about 30% of your total deployed CPU capacity and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth o' as a result of zone failure while you avoid unnecessary costs How should you prepare to handle the predicted growth?

A. Because you are only using 30% of deployed CPU capacity there is significant headroom and you do not need to add any additional capacity for this rate of growth
B. Proactively add 80% more node capacity to account for six months of 10% growth rate and then perform a load test to ensure that you have enough capacity
C. Verify the maximum node pool size enable a Horizontal Pod Autoscaler and then perform a load lest to verify your expected resource needs
D. Because you deployed the service on GKE and are using a cluster autoscaler your GKE cluster will scale automatically regardless of growth rate

Answer: C

NEW QUESTION # 51
Your organization has a containerized web application that runs on-premises As part of the migration plan to Google Cloud you need to select a deployment strategy and platform that meets the following acceptance criteria
1 The platform must be able to direct traffic from Android devices to an Android-specific microservice
2 The platform must allow for arbitrary percentage-based traffic splitting
3 The deployment strategy must allow for continuous testing of multiple versions of any microservice What should you do?

A. Deploy the canary release to Google Kubernetes Engine with Anthos Sen/ice Mesh Use traffic splitting to direct 10% of user traffic to the new version based on the user-agent header configured in the virtual service
B. Deploy the canary release of the application to Compute Engine Use Anthos Service Mesh with Compute Engine to direct 10% of user traffic to the canary release by configuring the virtual service.
C. Deploy the canary release of the application to Cloud Run Use traffic splitting to direct 10% of user traffic to the canary release based on the revision tag
D. Deploy the canary release of the application to App Engine Use traffic splitting to direct a subset of user traffic to the new version based on the IP address

Answer: A

Explanation:
The best option for deploying a containerized web application to Google Cloud with the given acceptance criteria is to use Google Kubernetes Engine (GKE) with Anthos Service Mesh. GKE is a managed service for running Kubernetes clusters on Google Cloud, and Anthos Service Mesh is a service mesh that provides observability, traffic management, and security features for microservices. With Anthos Service Mesh, you can use traffic splitting to direct traffic from Android devices to an Android-specific microservice by configuring the user-agent header in the virtual service. You can also use traffic splitting to direct arbitrary percentage-based traffic to different versions of any microservice for continuous testing. For example, you can use a canary release strategy to direct 10% of user traffic to a new version of a microservice and monitor its performance and reliability.

NEW QUESTION # 52
You are responsible for creating and modifying the Terraform templates that define your Infrastructure. Because two new engineers will also be working on the same code, you need to define a process and adopt a tool that will prevent you from overwriting each other's code. You also want to ensure that you capture all updates in the latest version. What should you do?

A. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.
* At the end of each day, confirm that all changes have been captured in the files within the folder structure and create a new .zip archive with a predefined naming convention.
* Upload the .zip archive to a versioned Cloud Storage bucket and accept it as the latest version.
B. * Store your code in a Git-based version control system.
* Establish a process that allows developers to merge their own changes at the end of each day.
* Package and upload code lo a versioned Cloud Storage bucket as the latest master version.
C. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.
* At the end of each day. confirm that all changes have been captured in the files within the folder structure.
* Rename the folder structure with a predefined naming convention that increments the version.
D. * Store your code in a Git-based version control system.
* Establish a process that includes code reviews by peers and unit testing to ensure integrity and functionality before integration of code.
* Establish a process where the fully integrated code in the repository becomes the latest master version.

Answer: B

NEW QUESTION # 53
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