ExamPrep Google PCA

Section 1: Designing and planning a cloud solution architecture (~25% of the exam)

1.1 Designing a cloud solution infrastructure that meets business requirements

• Business use cases and product strategy
• Identifying functional and non-functional requirements
• Business continuity plan
• Cost optimization
• Supporting the application design
• Integration patterns with external systems
• Movement of data
• Design decision trade-offs
• Workload disposition strategies (e.g., build, buy, modify, or deprecate)
• Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], and metrics)
• Security and compliance
• Observability

1.2 Designing a cloud solution infrastructure that meets technical requirements

• Familiarity with the Google Cloud Well-Architected Framework
• High availability and fail-over design
• Flexibility of cloud resources
• Scalability to meet growth requirements
• Performance and latency
• Gemini Cloud Assist
• Backup and recovery

1.3 Designing network, storage, and compute resources

• Integration with on-premises/multicloud environments
• Google Cloud AI and machine learning solutions (e.g., Gemini LLMs, Agent Builder, Model Garden, Gemini models, and AI Hypercomputer)
• Cloud-native networking (e.g., virtual private cloud [VPC], peering, firewalls, load balancers, routing, container networking, shared VPC, and Private Service Connect)
• Choosing data processing solutions
• Choosing appropriate storage types (e.g., object, file, and databases)
• Mapping compute needs to platform products (e.g., Google Kubernetes Engine [GKE], Cloud Run, and Cloud Run functions)
• Choosing compute resources (e.g., spot VMs, custom machine types, and specialized workload)

1.4 Creating a migration plan (documents and architectural diagrams)

• Integrating solutions with existing systems
• Assessing and migrating systems and data to support the solution (e.g., Google Cloud Migration Center)
• Using migration methodologies, workload testing, network planning, and dependency planning
• Determining software license implications and financial impact

1.5 Envisioning future solution improvements

• Cloud and technology improvements
• Evolution of business needs
• Cloud-first design approach

Section 2: Managing and provisioning a cloud solution infrastructure (~17.5% of the exam)

2.1 Configuring network topologies

• Extending to on-premises environments (hybrid networking)
• Extending to a multicloud environment that may include Google Cloud-to-Google Cloud communication
• Security protection (e.g., intrusion protection, access control, and firewalls)
• VPC design and load balancing (e.g., access to cloud, internet, and cloud-adjacent service)

2.2 Configuring individual storage systems

• Data storage allocation
• Data processing and compute provisioning
• Security and access management
• Configuration for data transfer and latency
• Data retention and data lifecycle management
• Data growth planning
• Data protection (e.g., backup and recovery)

2.3 Configuring compute systems

• Compute resource provisioning
• Compute volatility configuration (spot vs. standard)
• Cloud-native network configuration for compute resources (e.g., Compute Engine, GKE, serverless networking, and Google Cloud VMware Engine)
• Infrastructure orchestration, resource configuration, and patch management
• Container orchestration
• Serverless computing

2.4 Leveraging Vertex AI for end-to-end ML workflows

• Using Vertex AI pipelines to automate and orchestrate the ML lifecycle
• Preparing for Vertex AI data integration
• Using AI Hypercomputer (e.g., using AI Hypercomputer, Cloud Run functions, and Vertex AI for ML/AI workloads; integrating GPUs and TPUs in ML model training and serving; optimizing for different consumption models; and running large-scale AI model trainings)

2.5 Configuring prebuilt solutions or APIs with Vertex AI

• Differentiating between the Google AI APIs (e.g., Search, Conversation, Vision, Image, Video, and Audio)
• Integrating Gemini Enterprise features (AI Agents and NotebookLM) to enhance workflows
• Integrating AI models from Model Garden into the solution

Section 3: Designing for security and compliance (~17.5% of the exam)

3.1 Designing for security

• Identity and Access Management (IAM)
• Resource hierarchy (organizations, folders, and projects)
• Data security (key management, encryption, secret management)
• Separation of duties
• Security controls (e.g., auditing, VPC Service Controls, context aware access, organization policy, and hierarchical firewall policy)
• Managing customer-managed encryption keys with Cloud Key Management Service (Cloud KMS)
• Secure remote access (e.g., Identity-Aware Proxy, service account impersonation, Chrome Enterprise Premium, and Workload Identity Federation)
• Securing software supply chain
• Securing AI (e.g., Model Armor, Sensitive Data Protection, and secure model deployment)

3.2 Designing for compliance

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

Section 4: Analyzing and optimizing technical and business processes (~15% of the exam)

4.1 Analyzing and defining technical processes

• Software development lifecycle (SDLC)
• Continuous integration/continuous deployment
• Troubleshooting/root cause analysis best practices
• Testing and validation of software and infrastructure
• Service catalog and provisioning
• Disaster recovery

4.2 Analyzing and defining business processes

• Stakeholder management (e.g., influencing and facilitation)
• Change management
• Team assessment/skills readiness
• Decision-making processes
• Customer success management
• Cost optimization/resource optimization (CapEx/OpEx)
• Business continuity

Section 5: Managing implementation (~12.5% of the exam)

5.1 Advising development and operation teams to ensure the successful deployment of the solution

• Application and infrastructure deployment
• API management best practices (e.g., Apigee)
• Testing frameworks (load/unit/integration)
• Data and system migration and management tooling
• Gemini Cloud Assist

5.2 Interacting with Google Cloud programmatically

• Cloud Shell Editor, Cloud Code, and Cloud Shell Terminal
• Google Cloud SDKs (e.g., gcloud, gsutil, and bq)
• Cloud Emulators (e.g., Bigtable, Spanner, Pub/Sub, and Firestore)
• Infrastructure as Code (e.g., IaC and Terraform)
• Accessing Google API best practices
• Google API client libraries

Section 6: Ensuring solution and operations excellence (~12.5% of the exam)

6.1 Understanding the principles and recommendations of the operational excellence pillar of the Google Cloud Well-Architected Framework

6.2 Familiarity with Google Cloud Observability solutions

• Monitoring and logging
• Profiling and benchmarking
• Alerting strategies

6.3 Deployment and release management

6.4 Assisting with the support of deployed solutions

6.5 Evaluating quality control measures

6.6 Ensuring the reliability of solutions in production

Examples include:

• Chaos engineering
• Penetration testing
• Load testing