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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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최신 Google Cloud Certified Professional-Cloud-Architect 무료샘플문제 (Q146-Q151):

질문 # 146
Your company wants to optimize Google Cloud costs for their development and staging environments. These environments are workstations used by developers Monday through Friday, 9:00 AM to 6:00 PM local time.
Currently, the environments run on a fleet of n1-standard-4 Compute Engine instances that operate 24/7, leading to a high monthly cost for resources that are idle more than 70% of the time. You need to implement a solution that significantly reduces the monthly cost of these non-production environments without impacting the development team ' s productivity during work hours. What should you do?

A. Re-architect the environments to run on a regional managed instance group (MIG) with autoscaling enabled.
B. Upgrade all instances to the N2 machine series.
C. Schedule the virtual machines to start and stop to match your team ' s work schedule.
D. Purchase three-year committed use discounts (CUDs) for the existing n1-standard-4 instances.

정답：C

설명：
The most direct and impactful way to reduce costs for predictable, time-bound workloads is to stop resources when they are not in use . According to the Google Cloud Architecture Framework: Cost Optimization , Compute Engine instances are billed per second while running. By scheduling instances to run only from 9:00 AM to 6:00 PM on weekdays, the instances are active for only 45 hours out of a 168-hour week. This results in a cost reduction of approximately 73% .
While Committed Use Discounts (CUDs) (Option B) provide significant savings (up to 57-70%), they require paying for the resources 24/7 for the duration of the commitment. Even with a 70% discount, paying for 24/7 usage is more expensive than paying full price for only 27% of the week. Autoscaling (Option C) is generally more effective for handling fluctuating traffic loads rather than developer workstations, which are often stateful or individual-specific. Instance Scheduling is a native Compute Engine feature that allows administrators to define start and stop operations automatically, ensuring that productivity is maintained by having the machines ready when developers start their day, while maximizing savings during off-hours.

질문 # 147
You are designing an application for use only during business hours. For the minimum viable product release, you'd like to use a managed product that automatically "scales to zero" so you don't incur costs when there is no activity. Which primary compute resource should you choose?

A. Cloud Functions
B. Kubernetes Engine
C. Compute Engine
D. AppEngine flexible environment

정답：A

설명：
Remember that Cloud Functions is serverless and scales from zero to scale and back to zero as the demand changes.

질문 # 148
You have a Python web application with many dependencies that requires 0.1 CPU cores and
128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

A. Perform the following:
1. Create a managed instance group with n1-standard-1 type machines.
2. Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
B. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3. Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.
C. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-4 type machines.
2. Build a Docker image from the master branch will all of the dependencies, and tag it with
"latest".
3. Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to
"Always". Restart the pods to automatically deploy new production releases.
D. Perform the following:
1. Create a managed instance group with f1-micro type machines.
2. Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3. Restart the instances to automatically deploy new production releases.

정답：A

질문 # 149
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

A. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
B. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
C. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
D. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months.
For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

정답：C

질문 # 150
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 a snapshot of the existing disk. Create an instance template from the snapshot. Create an autoscaled managed instance group from the instance template.
B. 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.
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 custom image from the existing disk. Create an instance template from the custom image. Create an autoscaled managed instance group from the instance template.

정답：C

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

질문 # 151
......

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