Free Practice Questions for Google Professional Cloud Architect Certification

Considerations include:

- 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

Considerations include:

- 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

Considerations include:

- 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)

Considerations include:

- 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

Considerations include:

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

Considerations include:

- 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 services)

Considerations include:

- 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)

Considerations include:

- 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

Considerations include:

- 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)

Considerations include:

- 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

Considerations include:

- 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)

Considerations include:

- 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)

Considerations include:

- 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

Considerations include:

- 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

Considerations include:

- 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

Considerations include:

- 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

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

Considerations include:

- Monitoring and logging - Profiling and benchmarking - Alerting strategies

Deployment and release management

Assisting with the support of deployed solutions

Evaluating quality control measures

Ensuring the reliability of solutions in production (e.g., chaos engineering, penetration testing, and load testing)