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

Section	Objectives
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
Implement security and compliance	- Compliance and governance 1. Audit logging and policy enforcement - Secure CI/CD pipelines 1. Secret and credential management 2. IAM and least privilege access
Implement site reliability engineering (SRE) practices	- Incident management and postmortems 1. Alerting and on-call practices - Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies
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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?

A. Deploy the service in one region and use a global load balancer to route traffic to this region.
B. Validate that the resource requirements are within the available quota limits of each region.
C. Use the n1-highcpu-96 machine type in the configuration of the MIG.
D. Monitor results of Stackdriver Trace to determine the required amount of resources.

Answer: B

Explanation:
https://cloud.google.com/compute/quotas#understanding_quotas
https://cloud.google.com/compute/quotas

NEW QUESTION # 118
Your team is building a service that performs compute-heavy processing on batches of data The data is processed faster based on the speed and number of CPUs on the machine These batches of data vary in size and may arrive at any time from multiple third-party sources You need to ensure that third parties are able to upload their data securely. You want to minimize costs while ensuring that the data is processed as quickly as possible What should you do?

A. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate identity and Access Management (1AM) access to the bucket
* Create a Cloud Function with a google, storage, object .finalise Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes
B. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services one that processes the batches of data and one that monitors Cloud Storage for new batches of data
* Stop the processing service when there are no batches of data to process
C. * Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that third parties can upload batches of data and provide appropriate credentials to the server
* Create a Cloud Function with a google.storage, object, finalize Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes
D. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data
* Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing

Answer: A

Explanation:
Explanation
The best option for ensuring that third parties are able to upload their data securely and minimizing costs while ensuring that the data is processed as quickly as possible is to provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket; create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger; write code so that the function can scale up a Compute Engine autoscaling managed instance group; use an image pre-loaded with the data processing software that terminates the instances when processing completes. A Cloud Storage bucket is a resource that allows you to store and access data in Google Cloud. You can provide a Cloud Storage bucket so that third parties can upload batches of data securely and conveniently. You can also provide appropriate IAM access to the bucket by using roles and policies to control who can read or write data to the bucket. A Cloud Function is a serverless function that executes code in response to an event, such as a change in a Cloud Storage bucket. A google.storage.object.finalize trigger is a type of trigger that fires when a new object is created or an existing object is overwritten in a Cloud Storage bucket. You can create a Cloud Function with a google.storage.object.finalize trigger so that the function runs whenever a new batch of data is uploaded to the bucket. You can write code so that the function can scale up a Compute Engine autoscaling managed instance group, which is a group of VM instances that automatically adjusts its size based on load or custom metrics. You can use an image pre-loaded with the data processing software that terminates the instances when processing completes, which means that the instances only run when there is data to process and stop when they are done. This way, you can minimize costs while ensuring that the data is processed as quickly as possible.

NEW QUESTION # 119
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to the production environment. A recent security audit alerted your team that the code pushed to production could contain vulnerabilities and that the existing tooling around virtual machine (VM) vulnerabilities no longer applies to the containerized environment. You need to ensure the security and patch level of all code running through the pipeline. What should you do?

A. Implement static code analysis tooling against the Docker files used to create the containers.
B. Set up Container Analysis to scan and report Common Vulnerabilities and Exposures.
C. Reconfigure the existing operating system vulnerability software to exist inside the container.
D. Configure the containers in the build pipeline to always update themselves before release.

Answer: A

NEW QUESTION # 120
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. Because you deployed the service on GKE and are using a cluster autoscaler your GKE cluster will scale automatically regardless of growth rate
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. 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

Answer: C

Explanation:
Explanation
The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.

NEW QUESTION # 121
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
B. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: // [bucket]
.srorage.googleapis.com/ [holnchart] as the Helm repository
C. Store public and private charts in OCI format by using Artifact Registry
D. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider

Answer: C

Explanation:
Explanation
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

NEW QUESTION # 122
......

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