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Topics of Google Professional Cloud Architect Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our Google Professional Cloud Architect Dumps will include the following topics:

1. Designing and planning a cloud solution architecture

Business requirements considerations

• Supporting the application design
• Compliance and observability
• Integration with external systems
• Movement of data
• Build, buy or modify
• Business use cases and product strategy
• Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)
• Design decision trade-offs
• Cost optimization

Technical requirements considerations

• Scalability to meet growth requirements
• High availability and failover design
• Elasticity of cloud resources
• Performance and latency

Network, Storage, and Compute resources Considerations

• Mapping compute needs to platform products
• Cloud-native networking (VPC, peering, firewalls, container networking)
• Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)
• Choosing data processing technologies
• Integration with on- premises/multi- cloud environments
• Choosing appropriate storage types (e.g., object, file, RDBMS, NoSQL, NewSQL)

Creating a migration plan

• Dependency management planning
• Testing and proof of concept
• Integrating solution with existing systems
• Licensing mapping
• Migrating systems and data to support the solution
• Network planning

Envisioning future solution improvements

• Business needs evolution
• Evangelism and advocacy
• Cloud and technology improvements

2. Managing and provisioning a solution Infrastructure

Configuring network topologies

• Extending to a multi-cloud environment that may include GCP to GCP communication
• Extending to on-premises (hybrid networking)
• Security and data protection

Configuring individual storage systems

• Security and access management
• Data retention and data life cycle management
• Data storage allocation
• Data processing/compute provisioning
• Network configuration for data transfer and latency
• Data growth management

Configuring compute systems

• Container orchestration with Kubernetes
• Compute system provisioning
• Network configuration for compute nodes
• Infrastructure provisioning technology configuration (e.g. Chef/Puppet/Ansible/Terraform/Deployment Manager)
• Compute volatility configuration (preemptible vs. standard)

3. Designing for security and compliance

Security considerations

• Data security (key management, encryption)
• Separation of duties (SoD)
• Identity and access management (IAM)
• Security controls (e.g., auditing, VPC Service Controls organization policy)
• Resource hierarchy (organizations, folders, projects)
• Managing customer- managed encryption keys with Cloud KMS
• Penetration testing

compliance Considerations

• Industry certifications (e.g., SOC 2)
• Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])
• Legislation (e.g., health record privacy, children's privacy, data privacy, and ownership)
• Audits (including logs)

4. Analyzing and optimizing technical and business processes

Analyzing and defining technical processes considerations

• Continuous integration / continuous deployment
• Troubleshooting / post mortem analysis culture
• Business continuity and disaster recovery
• Testing and validation
• Service catalog and provisioning
• Software development life cycle plan (SDLC)

Analyzing and defining business processes. Considerations include:

• Team assessment / skills readiness
• Change management
• Customer success management
• Decision- making process
• Stakeholder management (e.g. influencing and facilitation)
• Cost optimization / resource optimization (capex / opex)

Developing procedures to ensure resilience of solution in production (e.g., chaos engineering)

5. Managing implementation

Advising development/operation team(s) to ensure successful deployment of the solution. considerations

• Data and system migration tooling
• Testing frameworks (load/unit/integration)
• API best practices
• Application development

Interacting with Google Cloud using GCP SDK (gcloud, gsutil, and bq) considerations

• Google Cloud Shell
• Local installation

6. Ensuring solution and operations reliability

• Deployment and release management
• Monitoring/logging/profiling/alerting solution
• Evaluating quality control measures
• Assisting with the support of solutions in operation

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What It Is to Be Google Professional Cloud Architect Certified?

Earning this certificate allows an individual to gain an expert-like understanding of concepts like building the cloud solution architecture and its end-to-end implementation, managing and provisioning the cloud solution infrastructure, and security & compliance of Google cloud.

Accredited professionals are well-versed in everything related to enterprise cloud strategy, architecture planning, and solution designing. They are known to be proficient in the development of methodologies and best practices that are needed for multiple cloud or hybrid environments.

Reference: https://cloud.google.com/certification/cloud-architect

Ensuring Operations and Solution Reliability

• Deploy and release management;
• Evaluate quality control measures.
• Assist with solutions support within the operation;
• Monitor, log, profile, and alert solutions;

Managing & Provisioning Solution Infrastructures

• 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.
• 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 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;

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