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Google Cloud Professional Cloud Architect Exam Changes October 2025: Key Updates You Need to Know

By Shing Lyu    

Disclaimer: This content reflects my personal opinions, not those of any organizations I am or have been affiliated with. Code samples are provided for illustration purposes only, use with caution and test thoroughly before deployment.

While working on my recent exploration of Rust on Google Cloud Run, I was impressed by how seamless and developer-friendly the experience turned out to be. The container-based approach provided more flexibility and better tooling integration than I expected, sparking my interest in Google Cloud’s broader ecosystem and architectural patterns.

This hands-on experience led me to consider pursuing the Professional Cloud Architect certification. As I began researching study materials and exam guides, I discovered that Google Cloud was releasing an updated version of the certification exam in October 2025. Rather than rush into the old version, I decided to wait and understand what changes were coming. The updates include new AI-focused content and formal incorporation of the Well-Architected Framework principles.

TL;DR

What’s Actually Changing?

The October 30, 2025 update to the Google Cloud Professional Cloud Architect exam (version 6.1) brings some notable changes that I think you should know about. Two main themes run through these updates: Google’s Well-Architected Framework (WAF) is now part of the official exam content, and AI/ML services get much more coverage across different sections.

Here’s what’s new: section 6.1 has been rewritten to focus on the WAF’s operational excellence pillar (previously this section covered generic monitoring/logging), so you’ll need to know these principles as part of your core knowledge. The AI expansion is significant - they’ve added coverage of Vertex AI, Gemini models, AI Hypercomputer, and Model Garden throughout multiple sections instead of keeping AI content in isolated sections. They’ve created two brand new sections (2.4 and 2.5) that focus specifically on Vertex AI workflows and pre-built AI solutions.

The security domain now includes “Securing AI” as its own topic, covering things like Model Armor, Sensitive Data Protection, and how to deploy AI models securely. The exam retains the EHR Healthcare case study while replacing Helicopter Racing League, Mountkirk Games, and TerramEarth with three new scenarios: Altostrat Media, Cymbal Retail, and KnightMotives Automotive. All case studies now incorporate AI integration as core business requirements.

Your existing certification stays valid until its normal expiration date, and these changes reflect how AI services and structured architecture approaches have become standard practice.

The Two Big Changes You Need to Know About

Well-Architected Framework is Now Required Knowledge

The biggest shift in version 6.1 is that Google’s Well-Architected Framework moved from optional best practice to required knowledge. Previously mentioned as guidance, WAF principles now appear throughout the exam, with operational excellence getting its own section (6.1). This aligns with how the updated role description emphasizes “efficient, cost-effective, and flexible solutions” as core skills.

The framework has six pillars - operational excellence, security, reliability, cost optimization, performance efficiency, and sustainability - and now they’re used as evaluation criteria across all your architectural decisions instead of being optional knowledge. You’ll need to understand how your choice of services, design patterns, and implementation approaches affects the overall quality of your architecture. The emphasis on “profiling and benchmarking” plus “load testing” means performance optimization is now about making decisions based on real data rather than just theoretical capacity planning.

This change means you need to think about business considerations too. Understanding cost optimization strategies, how to run operations smoothly, and sustainability factors means you’ll need to consider business impact and long-term operational sustainability, not just the technical implementation. The framework’s approach to continuous improvement aligns with DevOps culture and site reliability engineering principles that most companies use now. You’ll need to show you understand monitoring strategies, incident response procedures, and automated ways to fix problems that keep systems reliable and performing well over time.

AI and ML Content Gets Major Expansion

The AI and ML content expansion throughout the exam is a significant addition to what you need to know. Version 6.1 adds two entirely new sections focused on AI: Section 2.4 covers “Leveraging Vertex AI for end-to-end ML workflows,” while Section 2.5 is about “Configuring pre-built solutions or APIs with Vertex AI.” This structural addition shows that AI knowledge is now considered standard architectural knowledge rather than specialized domain expertise.

The expanded AI coverage includes Vertex AI Pipelines for MLOps workflows, AI Hypercomputer for large-scale training workloads, and GPUs and TPUs as standard compute options. You’ll need to understand Gemini LLMs and their enterprise features, Agent Builder for conversational AI applications, and Model Garden for accessing and deploying pre-trained models.

This breadth reflects how current architectures increasingly include AI capabilities across multiple layers, from data processing and analytics to user interfaces and automation systems.

The security section now includes “Securing AI” as a specific topic covering Model Armor, Sensitive Data Protection, and secure model deployment. AI introduces unique security challenges that traditional cloud security approaches don’t fully address, including protecting models, securing training data, inference endpoint security, and privacy implications of AI-powered features.

You’ll need to understand how to implement security principles specifically for AI workloads, protect sensitive training data throughout the ML lifecycle, and ensure that AI models are protected from attacks and unauthorized access.

Gemini Cloud Assist appears throughout multiple sections, showing that AI-powered development and operational tools are becoming standard practice. This includes understanding how to use AI for code generation, infrastructure optimization, troubleshooting help, and automated documentation. The expectation is that architects will both design AI-enabled applications and use AI tools to improve their own productivity and decision-making.

Updated Case Studies and What They Mean

The case studies have been updated to include EHR Healthcare (retained from the previous version) along with three new scenarios: Altostrat Media, Cymbal Retail, and KnightMotives Automotive. All case studies now incorporate AI integration as core business requirements.

The media scenario likely tests content processing pipelines and AI-powered content analysis at scale, requiring understanding of Cloud Run for containerized workloads, BigQuery for analytics, and Vertex AI for content generation. The retail case probably addresses real-time personalization engines and inventory optimization through machine learning while maintaining compliance with privacy regulations. The automotive scenario appears focused on edge computing architectures and IoT data ingestion for real-time decision systems.

You’ll need to understand how to architect complete solutions that integrate AI capabilities with traditional cloud infrastructure, data pipelines, security controls, and operational monitoring. The scenarios will test your ability to balance AI-specific requirements like model training and inference latency with traditional concerns like cost optimization, security compliance, and operational reliability.

Security Updates You Should Care About

Security has expanded beyond traditional approaches to include zero-trust architectures, supply chain security, and AI-specific threats. Key updates include:

The new “Securing AI” section covers Model Armor (protection against adversarial attacks), Sensitive Data Protection for training data, and secure model deployment. AI workloads have different threat profiles than traditional applications, requiring specialized security measures for model integrity, data privacy, and inference protection.

Infrastructure and Development Updates

Infrastructure-as-code is now explicitly required knowledge. The exam expects understanding of:

How This Changes Your Study Strategy

If you’ve been preparing with older materials, you’ll need to adjust your approach:

Well-Architected Framework: Study how each service contributes to the six WAF pillars. Shift from memorizing features to understanding design principles, cost implications, and operational considerations.

AI/ML Focus: Get hands-on with Vertex AI - build ML pipelines, deploy models through Model Garden, and integrate AI services. Use Google Cloud Skills Boost for labs and the Vertex AI documentation for implementation guides.

Case Studies: Practice architectural decision-making across industries rather than memorizing scenarios. The official case study materials provide hands-on practice.

Security: Understand security implications for every architectural decision. Practice with IAM, hierarchical firewall policies, and AI workload security. Check the Google Cloud Architecture Framework security section.

Infrastructure as Code: Terraform is now required knowledge. Use the Google Cloud Provider documentation for common patterns. Practice with API integration and performance optimization tools like Cloud Monitoring and Cloud Profiler.

Wrapping Up

The October 2025 updates to the Google Cloud Professional Cloud Architect exam include content additions that reflect current trends in cloud architecture. The addition of the Google Cloud Well-Architected Framework as explicit exam content and the expanded coverage of AI services throughout all domains indicate that these areas have become important parts of cloud architecture practice.

For aspiring candidates, these changes provide guidance on developing skills that align with industry expectations and job market demands. The emphasis on hands-on experience with Vertex AI, infrastructure as code, and comprehensive security practices means that certified professionals should be prepared for real-world architectural challenges. The new case studies provide industry-specific contexts that prepare candidates for the types of problems they’ll encounter in professional practice.

The transition provides an opportunity for the cloud community to adopt more systematic approaches to architectural decision-making. The Well-Architected Framework provides common vocabulary and evaluation criteria that can improve communication between technical teams, business stakeholders, and operational groups. As more professionals become certified under the new standards, organizations benefit from more consistent architectural practices and better alignment between technical implementation and business objectives.

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