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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q339-Q344):

NEW QUESTION # 339
You are using a GitHub repository for your application's source code. You want to set up an efficient and secure continuous deployment process to automatically build and deploy the application to Cloud Run whenever a pull request is merged. What should you do?

A. Create a GitHub webhook trigger in Cloud Build. Once a pull request is merged, trigger Cloud Build to build a container image and save it in Artifact Registry. Use Config Sync to deploy the application to Cloud Run.
B. Create a GitHub Enterprise trigger in Cloud Build. Once a pull request is merged, trigger Cloud Build to build and deploy the application to Cloud Run. Save the deployment credential to Secret Manager.
C. Create a workflow using GitHub Actions to build and deploy the application to Cloud Run once a pull request is merged. The workflow will use a service account key checked in with your source code for deployment permission.
D. Connect your repository using the Cloud Build GitHub app. Create a trigger in Cloud Build. Once a pull request is merged, trigger Cloud Build to build and deploy the application to Cloud Run.

Answer: D

Explanation:
Connecting the repository using the Cloud Build GitHub app and using a Cloud Build trigger provides an automated, secure CI/CD path without storing long-lived credentials in the repo.
Cloud Build can build the container and deploy directly to Cloud Run using Google-recommended identity-based authentication and managed integrations when a merge event occurs.

NEW QUESTION # 340
Your marketing department wants to send out a promotional email campaign. The development team wants to minimize direct operation management. They project a wide range of possible customer responses, from 100 to 500,000 click-throughs per day. The link leads to a simple website that explains the promotion and collects user information and preferences. Which infrastructure should you recommend? (CHOOSE TWO)

A. Use a single compute Engine virtual machine (VM) to host a web server, backed by Google Cloud SQL.
B. Use a managed instance group to serve the website and Google Cloud Bigtable to store user data.
C. Use Google App Engine to serve the website and Google Cloud Datastore to store user data.
D. Use a Google Container Engine cluster to serve the website and store data to persistent disk.

Answer: B,C

Explanation:
Reference:

NEW QUESTION # 341
You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud Bigtable.
Which three requirements should they include? Choose 3 answers

A. Schedule the load-testing tool to regularly run against the production environment.
B. Instrument the production services to record every transaction for replay by the load-testing tool.
C. Ensure all third-party systems your services use are capable of handling high load.
D. Create a separate Google Cloud project to use for the load-testing environment.
E. Ensure that the load tests validate the performance of Cloud Bigtable.
F. Instrument the load-testing tool and the target services with detailed logging and metrics collection.

Answer: A,C,D

Explanation:
Not E: Load testing the Bigtable is not necessary as Bigtable provisions and scales to hundreds of petabytes automatically and can smoothly handle millions of operations per second by default and it is completely Managed.
Not F: Performance testing should never be done on prod environment.

NEW QUESTION # 342
Your company is running its application workloads on Compute Engine. The applications have been deployed in production, acceptance, and development environments. The production environment is business-critical and is used 24/7, while the acceptance and development environments are only critical during office hours.
Your CFO has asked you to optimize these environments to achieve cost savings during idle times. What should you do?

A. Create a shell script that uses the gcloud command to change the machine type of the development and acceptance instances to a smaller machine type outside of office hours. Schedule the shell script on one of the production instances to automate the task.
B. Use Cloud Scheduler to trigger a Cloud Function that will stop the development and acceptance environments after office hours and start them just before office hours.
C. Deploy the development and acceptance applications on a managed instance group and enable autoscaling.
D. Use regular Compute Engine instances for the production environment, and use preemptible VMs for the acceptance and development environments.

Answer: B

Explanation:
Reference: https://cloud.google.com/blog/products/it-ops/best-practices-for-optimizing-your-cloud-costs

NEW QUESTION # 343
You are developing an application using different microservices that should remain internal to the cluster. You want to be able to configure each microservice with a specific number of replicas. You also want to be able to address a specific microservice from any other microservice in a uniform way, regardless of the number of replicas the microservice scales to. You need to implement this solution on Google Kubernetes Engine. What should you do?

A. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using an Ingress, and use the Ingress IP address to address the Deployment from other microservices within the cluster.
B. Deploy each microservice as a Pod. Expose the Pod in the cluster using an Ingress, and use the Ingress IP address name to address the Pod from other microservices within the cluster.
C. Deploy each microservice as a Pod. Expose the Pod in the cluster using a Service, and use the Service DNS name to address the microservice from other microservices within the cluster.
D. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using a Service, and use the Service DNS name to address it from other microservices within the cluster.

Answer: D

Explanation:
Explanation
https://kubernetes.io/docs/concepts/services-networking/ingress/

NEW QUESTION # 344
......

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