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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Managing implementation	- Interacting with Google Cloud programmatically 1. Infrastructure as Code (IaC) 2. APIs and client libraries 3. Cloud SDK and gcloud CLI - Advising development/operation team(s) to ensure successful deployment 1. Deployment strategies 2. Application development best practices 3. Testing and validation
Analyzing and optimizing technical and business processes	- Analyzing and defining technical processes 1. Incident management and troubleshooting 2. CI/CD pipeline design 3. Software development lifecycle (SDLC) - Analyzing and defining business processes 1. Stakeholder communication 2. Business KPIs and metrics 3. Cost management and optimization
Ensuring solution and operations reliability	- Assisting with support 1. Capacity planning and scaling 2. Incident response and troubleshooting - Deployment and release management 1. Release management and versioning 2. Deployment strategies and rollback - Monitoring/logging/profiling/alerting solution 1. Error reporting and alerting 2. Cloud Monitoring and Cloud Logging 3. Performance profiling and tracing - Evaluating quality control measures
Designing for security and compliance	- Designing for security 1. Identity and Access Management (IAM) 2. Network security 3. Data security and encryption 4. Application security - Designing for compliance 1. Data residency and sovereignty 2. Regulatory and compliance requirements 3. Audit logging and monitoring
Managing and provisioning a solution infrastructure	- Configuring individual storage systems 1. Datastore and Bigtable 2. Cloud SQL and Spanner 3. Cloud Storage configuration - Configuring network topologies 1. Private access and VPN configuration 2. Hybrid and multi-cloud connectivity 3. VPC design and peering - Configuring compute systems 1. Compute Engine configuration 2. Cloud Run and Cloud Functions 3. GKE configuration and management
Designing and planning a cloud solution architecture	- Creating a migration plan 1. Identifying workloads for migration 2. Refactoring and rearchitecting applications 3. Planning data migration - Enabling future cloud technology 1. Designing for scalability and elasticity 2. Implementing future-proof architectures - Designing solution infrastructure that meets business requirements 1. Migration planning 2. Business use cases and product strategy 3. Data and storage architecture 4. Cost optimization 5. Integration with existing systems 6. Networking and compute resources - Designing network, storage, and compute resources 1. Compute engine selection and configuration 2. Storage options and data lifecycle management 3. Network design including load balancing and firewall rules 4. GKE cluster and workload design

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q217-Q222):

NEW QUESTION # 217
For this question, refer to the Cymbal Retail case study. Cymbal ' s generative Al models require high- performance storage for temporary files generated during model training and inference. These files are ephemeral and frequently accessed and modified You need to select a storage solution that minimizes latency and cost and maximizes performance for generative Al workloads. What should you do?

A. Use Filestore to store temporary files
B. Use a Cloud Storage bucket in the same region as your virtual machines Configure lifecycle policies to delete files after processing
C. Use Local SSDs attached to the VMs running the generative Al models
D. Use performance persistent disks.

Answer: C

Explanation:
According to the Google Cloud Architecture Framework: Performance Excellence , Local SSDs are the optimal choice for " scratch " space, temporary files, and workloads requiring the lowest possible latency and highest IOPS. Unlike Persistent Disks (Option C) or Filestore (Option B), which are network-attached, Local SSDs are physically attached to the server that hosts the virtual machine instance.
This physical proximity eliminates network latency, providing sub-millisecond access times which are critical for " frequently accessed and modified " temporary files during AI training and inference. For generative AI, where data shuffling and checkpointing are intensive, the high throughput of Local SSDs prevents the storage layer from becoming a bottleneck for the expensive GPU/TPU accelerators.
While the data on Local SSDs is ephemeral (lost if the VM is deleted or undergoes certain maintenance events), this perfectly matches Cymbal ' s requirement for " temporary files. " Furthermore, Local SSDs can be more cost-effective than provisioning high-performance Persistent Disks of equivalent speed, as their performance is not tied to the disk size. Cloud Storage (Option A) is unsuitable for this specific use case due to the significantly higher latency of object-based storage compared to block-based local storage.

NEW QUESTION # 218
You are building a continuous deployment pipeline for a project stored in a Git source repository and want to ensure that code changes can be verified deploying to production. What should you do?

A. Use Spinnaker to deploy builds to production and run tests on production deployments.
B. Use Jenkins to build the staging branches and the master branch. Build and deploy changes to production for 10% of users before doing a complete rollout.
C. Use Jenkins to monitor tags in the repository. Deploy staging tags to a staging environment for testing.After testing, tag the repository for production and deploy that to the production environment.
D. Use Spinnaker to deploy builds to production using the red/black deployment strategy so that changes can easily be rolled back.

Answer: C

Explanation:
Reference: https://github.com/GoogleCloudPlatform/continuous-deployment-on-kubernetes/blob/master/ README.md

NEW QUESTION # 219
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space.
How can you remediate the problem with the least amount of downtime?

A. In the Cloud Platform Console, create a snapshot of the persistent disk restore the snapshot to a new larger disk, unmount the old disk, mount the new disk and restart the database service
B. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux
C. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine
D. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk
E. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.

Answer: E

Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
Reference: https://cloud.google.com/compute/docs/disks/add-persistent-disk

NEW QUESTION # 220
You have an application that will run on Compute Engine. You need to design an architecture that takes into account a disaster recovery plan that requires your application to fail over to another region in case of a regional outage. What should you do?

A. Deploy the application on two Compute Engine instance groups, each in separate project and a different region. Use the first instance group to server traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
B. Deploy the application on two Compute Engine instance groups, each in the same project but in a different region. Use the first instance group to serve traffic, and use the HTTP load balancing service to fail over to the standby instance group in case of a disaster.
C. Deploy the application on two Compute Engine instances in the same project but in a different region.
Use the first instance to serve traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
D. Deploy the application on a Compute Engine instance. Use the instance to serve traffic, and use the HTTP load balancing service to fail over to an instance on your premises in case of a disaster.

Answer: B

NEW QUESTION # 221
Your company is running a stateless application on a Compute Engine instance. The application is used heavily during regular business hours and lightly outside of business hours. Users are reporting that the application is slow during peak hours. You need to optimize the application's performance. What should you do?

A. Create an instance template from the existing disk. Create a custom image from the instance template. Create an autoscaled managed instance group from the custom image.
B. Create a custom image from the existing disk. Create an instance template from the custom image. Create an autoscaled managed instance group from the instance template.
C. Create a snapshot of the existing disk. Create a custom image from the snapshot. Create an autoscaled managed instance group from the custom image.
D. Create a snapshot of the existing disk. Create an instance template from the snapshot. Create an autoscaled managed instance group from the instance template.

Answer: C

Explanation:
https://cloud.google.com/compute/docs/instance-templates/create-instance-templates

NEW QUESTION # 222
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