The Google GCP-PCA exam preparation guide is designed to provide candidates with necessary information about the Professional Cloud Architect exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Professional Cloud Architect (GCP-PCA) exam.
It is recommended for all the candidates to refer the GCP-PCA objectives and sample questions provided in this preparation guide. The Google Professional Cloud Architect certification is mainly targeted to the candidates who want to build their career in Professional domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Professional Cloud Architect exam.
Google GCP-PCA Exam Summary:
| Exam Name | Google Professional Cloud Architect |
| Exam Code | GCP-PCA |
| Exam Price | $200 USD |
| Duration | 120 minutes |
| Number of Questions | 50-60 multiple choice and multiple select questions |
| Passing Score | Pass / Fail (Approx 70%) |
| Recommended Training / Books | Professional Cloud Architect Exam Learning Path |
| Schedule Exam | Google CertMetrics |
| Sample Questions | Google GCP-PCA Sample Questions |
| Recommended Practice | Google Cloud Platform - Professional Cloud Architect (GCP-PCA) Practice Test |
Google Cloud Architect Syllabus:
| Section | Objectives |
|---|---|
Designing and planning a cloud solution architecture (~25% of the exam) |
|
| Designing a cloud solution infrastructure that meets business requirements. 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 |
| Designing a cloud solution infrastructure that meets technical requirements. 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 |
| Designing network, storage, and compute resources. 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) |
| Creating a migration plan (i.e., documents and architectural diagrams). 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 |
| Envisioning future solution improvements. Considerations include: |
- Cloud and technology improvements - Evolution of business needs - Cloud-first design approach |
Managing and provisioning a cloud solution infrastructure (~17.5% of the exam) |
|
| Configuring network topologies. 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) |
| Configuring individual storage systems. 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) |
| Configuring compute systems. 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 |
| Leveraging Vertex AI for end-to-end ML workflows. 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) |
| Configuring prebuilt solutions or APIs with Vertex AI. 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 |
Designing for security and compliance (~17.5% of the exam) |
|
| Designing for security. 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) |
| Designing for compliance. 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) |
Analyzing and optimizing technical and business processes (~15% of the exam) |
|
| Analyzing and defining technical processes. 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 |
| Analyzing and defining business processes. 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 |
Managing implementation (~12.5% of the exam) |
|
| Advising development and operation teams to ensure the successful deployment of the solution. 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 |
| Interacting with Google Cloud programmatically. 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 |
Ensuring solution and operations excellence (~12.5% of the exam) |
|
| Understanding the principles and recommendations of the operational excellence pillar of the Google Cloud Well-Architected Framework | |
| Familiarity with Google Cloud Observability solutions. 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) | |