Managing & Provisioning Solution Infrastructures
- Configure individual storage systems: the areas of focus should include data storage allocation; access management and security; data processing and compute provisioning; data lifecycle management and data retention; network configuration for the data latency and transfer;
- Configure network topologies: the examinees should have the ability to extend to hybrid and on-premises networking and multi-Cloud environment that may entail GCP to GCP communication. It also requires their understanding of data protection and security;
- Configure compute systems: you should understand system provisioning; compute volatility configuration; container orchestration using Kubernetes; technology configuration for infrastructure provisioning; network configuration for the compute nodes.
Reference: https://cloud.google.com/certification/cloud-architect
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The Google Professional-Cloud-Architect exam covers a wide range of topics, including designing and planning a cloud solution architecture, managing and provisioning the cloud infrastructure, optimizing the cloud solution for performance and cost, and ensuring security and compliance. Candidates are required to have a deep understanding of GCP services such as Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, and BigQuery, among others. They must also have experience in designing and implementing solutions that are scalable, reliable, and secure.
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Google Professional-Cloud-Architect Exam Syllabus Topics:
| Section | Weight | Objectives |
| Designing for security and compliance | 17.5% | - Securing data and communications
- 1. Key management and secret security
- 2. Encryption at rest and in transit
- Implementing identity and access management
- 1. Organizational policies and resource hierarchy
- 2. IAM roles, permissions, and service accounts
- Enforcing compliance and governance
- 1. VPC Service Controls and security perimeters
- 2. Security controls, auditing, and logging
|
| 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. Load balancing and CDN configuration
- 2. VPC design, peering, and hybrid connectivity
- Managing storage and data services
- 1. Cloud Storage, Filestore, Persistent Disk management
- 2. Database deployment, replication, and backup
|
| Managing implementation | 12.5% | - Guiding development and deployment teams
- 1. CI/CD pipelines and automation
- 2. Application and infrastructure deployment strategies
- Testing and validating solutions
- 1. Disaster recovery and failover testing
- 2. Functional, performance, and security testing
- Using Google Cloud tools and APIs programmatically
- 1. Infrastructure as Code (Terraform, Deployment Manager)
- 2. Cloud SDK, Cloud Shell, and Cloud Code
|
| Designing and planning a cloud solution architecture | 25% | - Evaluating and selecting Google Cloud services
- 1. Serverless and managed service adoption
- 2. Compute, storage, database, and networking service selection
- Designing cloud solution infrastructure aligned with business requirements
- 1. Scalability, high availability, and fault tolerance design
- 2. Cost optimization strategies
- 3. Compliance and regulatory requirements consideration
- Planning cloud migration and hybrid/multi-cloud environments
- 1. Data transfer and synchronization
- 2. Interoperability and integration design
- 3. Migration planning and execution
|
| Ensuring solution and operations excellence | 12.5% | - Applying Well-Architected Framework principles
- 1. Operational excellence, reliability, security, cost, and performance pillars
- Planning for continuous improvement
- 1. Technology updates and architecture evolution
- 2. Feedback loops and optimization cycles
- Implementing monitoring, logging, and observability
- 1. Alerting, dashboarding, and root cause analysis
- 2. Cloud Monitoring, Logging, and Error Reporting
|
| Analyzing and optimizing technical and business processes | 15% | - Improving reliability and performance
- 1. Latency reduction and throughput optimization
- 2. SLO, SLA, and error budgeting
- Streamlining operational workflows
- 1. Stakeholder communication and alignment
- 2. Change management and incident response
- Optimizing cost and resource efficiency
- 1. Resource right-sizing and usage monitoring
- 2. Committed use discounts and pricing models
|