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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ensuring solution and operations excellence	12.5%	- Applying Well-Architected Framework principles 1. Operational excellence, reliability, security, cost, and performance pillars - Implementing monitoring, logging, and observability 1. Alerting, dashboarding, and root cause analysis 2. Cloud Monitoring, Logging, and Error Reporting - Planning for continuous improvement 1. Feedback loops and optimization cycles 2. Technology updates and architecture evolution
Topic 2: Managing and provisioning solution infrastructure	15%	- Provisioning and managing compute resources 1. VMs, GKE, App Engine, Cloud Run deployment and management 2. Auto-scaling and resource management - Configuring network topologies and connectivity 1. VPC design, peering, and hybrid connectivity 2. Load balancing and CDN configuration - Managing storage and data services 1. Database deployment, replication, and backup 2. Cloud Storage, Filestore, Persistent Disk management
Topic 3: Designing and planning a cloud solution architecture	25%	- Planning cloud migration and hybrid/multi-cloud environments 1. Migration planning and execution 2. Data transfer and synchronization 3. Interoperability and integration design - Evaluating and selecting Google Cloud services 1. Compute, storage, database, and networking service selection 2. Serverless and managed service adoption - Designing cloud solution infrastructure aligned with business requirements 1. Compliance and regulatory requirements consideration 2. Scalability, high availability, and fault tolerance design 3. Cost optimization strategies
Topic 4: Analyzing and optimizing technical and business processes	15%	- Optimizing cost and resource efficiency 1. Committed use discounts and pricing models 2. Resource right-sizing and usage monitoring - Streamlining operational workflows 1. Stakeholder communication and alignment 2. Change management and incident response - Improving reliability and performance 1. Latency reduction and throughput optimization 2. SLO, SLA, and error budgeting
Topic 5: Designing for security and compliance	17.5%	- Enforcing compliance and governance 1. Security controls, auditing, and logging 2. VPC Service Controls and security perimeters - Securing data and communications 1. Encryption at rest and in transit 2. Key management and secret security - Implementing identity and access management 1. Organizational policies and resource hierarchy 2. IAM roles, permissions, and service accounts
Topic 6: Managing implementation	12.5%	- Testing and validating solutions 1. Disaster recovery and failover testing 2. Functional, performance, and security testing - Guiding development and deployment teams 1. CI/CD pipelines and automation 2. Application and infrastructure deployment strategies - Using Google Cloud tools and APIs programmatically 1. Infrastructure as Code (Terraform, Deployment Manager) 2. Cloud SDK, Cloud Shell, and Cloud Code

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Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q19-Q24):

質問 # 19
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.

正解：C

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

質問 # 20
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly.
What three steps should you take to diagnose the problem? Choose 3 answers.

A. Check inbound firewall rules for the network the machine is connected to
B. Delete the virtual machine (VM) and disks and create a new one
C. Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate
D. Connect the machine to another network with very simple firewall rules and investigate
E. Delete the instance, attach the disk to a new VM, and investigate
F. Take a snapshot of the disk and connect to a new machine to investigate

正解：A、C、F

解説：
D: Handling "Unable to connect on port 22" error message
Possible causes include:
* There is no firewall rule allowing SSH access on the port. SSH access on port 22 is enabled on all Compute Engine instances by default. If you have disabled access, SSH from the Browser will not work. If you run sshd on a port other than 22, you need to enable the access to that port with a custom firewall rule.
* The firewall rule allowing SSH access is enabled, but is not configured to allow connections from GCP Console services. Source IP addresses for browser-based SSH sessions are dynamically allocated by GCP Console and can vary from session to session.
F: Handling "Could not connect, retrying..." error
You can verify that the daemon is running by navigating to the serial console output page and looking for output lines prefixed with the accounts-from-metadata: string. If you are using a standard image but you do not see these output prefixes in the serial console output, the daemon might be stopped. Reboot the instance to restart the daemon.
Reference:
https://cloud.google.com/compute/docs/ssh-in-browser
https://cloud.google.com/compute/docs/ssh-in-browser

質問 # 21
You need to deploy a stateful workload on Google Cloud. The workload can scale horizontally, but each instance needs to read and write to the same POSIX filesystem. At high load, the stateful workload needs to support up to 100 MB/s of writes. What should you do?

A. Create a Cloud Filestore instance and mount it in each instance.
B. Create a Cloud Storage bucket and mount it in each instance using gcsfuse.
C. Use a persistent disk for each instance.
D. Use a regional persistent disk for each instance.

正解：A

解説：
Explanation
https://cloud.google.com/storage/docs/gcs-fuse#notes
Cloud Filestore: Cloud Filestore is a scalable and highly available shared file service fully managed by Google. Cloud Filestore provides persistent storage ideal for shared workloads. It is best suited for enterprise applications requiring persistent, durable, shared storage which is accessed by NFS or requires a POSIX compliant file system.
Reference: https://cloud.google.com/storage/docs/gcs-fuse

質問 # 22
Your company's user-feedback portal comprises a standard LAMP stack replicated across two zones. It is deployed in the us-central1 region and uses autoscaled managed instance groups on all layers, except the database. Currently, only a small group of select customers have access to the portal. The portal meets a 99.99% availability SLA under these conditions However next quarter, your company will be making the portal available to all users, including unauthenticated users. You need to develop a resiliency testing strategy to ensure the system maintains the SLA once they introduce additional user load. What should you do?

A. Expose the new system to a larger group of users, and increase group ' size each day until autoscale logic is tnggered on all layers. At the same time, terminate random resources on both zones.
B. Capture existing users input, and replay captured user load until resource utilization crosses 80%. Also, derive estimated number of users based on existing users usage of the app, and deploy enough resources to handle 200% of expected load.
C. Capture existing users input, and replay captured user load until autoscale is triggered on all layers. At the same time, terminate all resources in one of the zones.
D. Create synthetic random user input, replay synthetic load until autoscale logic is triggered on at least one layer, and introduce "chaos" to the system by terminating random resources on both zones.

正解：C

質問 # 23
Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions.
What should you do?

A. Configure IAM conditions to limit what resources can be configured.
B. Configure the quotas for resources in the regions not being used to 0.
C. Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other regions.
D. Configure an organizational policy which constrains where resources can be deployed.

正解：D

解説：
Topic 9, Helicopter Racing League Case
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
* Existing content is stored in an object storage service on their existing public cloud provider.
* Video encoding and transcoding is performed on VMs created for each job.
* Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
* Support ability to expose the predictive models to partners.
* Increase predictive capabilities during and before races:
# Race results
# Mechanical failures
# Crowd sentiment
* Increase telemetry and create additional insights.
* Measure fan engagement with new predictions.
* Enhance global availability and quality of the broadcasts.
* Increase the number of concurrent viewers.
* Minimize operational complexity.
* Ensure compliance with regulations.
* Create a merchandising revenue stream.
Technical requirements
* Maintain or increase prediction throughput and accuracy.
* Reduce viewer latency.
* Increase transcoding performance.
* Create real-time analytics of viewer consumption patterns and engagement.
* Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking).
Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.

質問 # 24
......

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