Free Professional-Cloud-Architect Practice Test Questions and Answers (2026) | Cert Empire

A microservice running on Cloud Run needs to connect to an external on-premises database via a Dedicated Interconnect connection. What is the required networking configuration for the Cloud Run service?

A Chief Security Officer (CSO) mandates that all network connections within the VPC network must be fully encrypted, even between internal services (VM-to-VM). The application is deployed on Compute Engine. What is the Google Cloud networking service that can enforce this?

GlobalTech requires a Disaster Recovery (DR) plan for their core e-commerce database (running on Cloud Spanner) with an aggressive Recovery Time Objective (RTO) and Recovery Point Objective (RPO) of under 5 minutes. The application must be available even if an entire region fails. Which Spanner configuration is required?

A security team needs to analyze network traffic patterns for auditing and anomaly detection. They require a complete record of all TCP/UDP traffic flowing through the VPC network, including source/destination IP, ports, and protocol. Which GCP feature should be enabled?

----Altostrat Media Case Study---- Company Overview Altostrat is a prominent player in the media industry, with an extensive collection of audio and video content that comprises podcasts, interviews, news broadcasts, and documentaries. Their success in delivering premium content to a diverse audience requires a content management system that can keep pace with the dynamic media landscape. Solution Concept Altostrat seeks to modernize its content management and user engagement strategies using Google Cloud's generative AI. They want a platform that empowers customers with personalized recommendations, natural language interactions, and seamless self-service support. Simultaneously, they want to drive revenue growth through dynamic pricing, targeted marketing, and personalized product suggestions. The seamless integration of AI-powered tools into their existing Google Cloud environment will enable Altostrat to efficiently manage their vast media library, enhance user experiences, and unlock new revenue streams. Google Cloud’s generative AI will solidify their leadership in the media industry. Existing Technical Environment Altostrat's content management and delivery platform leverages GKE for scalability and high availability, essential for handling their vast media library. Their extensive media library, spanning various documents, audio and video formats, is stored in Cloud Storage. To gain valuable insights into user behavior, content consumption patterns, and audience demographics, Altostrat leverages BigQuery as their primary data warehouse. Additionally, they use Cloud Run functions for serverless execution of event-driven tasks such as video transcoding, metadata extraction, and personalized content recommendations. While Altostrat has made significant strides in cloud adoption, they also maintain some legacy on-premises systems for specific workflows like content ingestion and archival. These systems are slated for modernization and migration to Google Cloud in the near future. User management and authentication are currently handled through a combination of Google Identity and third-party identity providers. For monitoring and observability, Altostrat relies on a mix of native Google Cloud tools like Cloud Monitoring and open-source solutions like Prometheus, with alerts primarily delivered via email notifications Business Requirements Accelerate and enhance the reliability of operational workflows across all environments. [Google Cloud + On-premises] ● Simplify infrastructure management for rapid application deployment. ● Optimize cloud storage costs while maintaining high availability and scalability for media content. ● Enable natural language interaction with the platform with 24/7 user support. ● Automatically generate concise summaries of media content. ● Extract rich metadata from media assets using NLP and computer vision. ● Detect and filter inappropriate content. ● Analyze media content to identify trends and extract insights. ● Inform content strategy and decision-making with data. Technical Requirements ● Modernize CI/CD for containerized deployments with a centralized management platform. ● Secure, high-performance hybrid cloud connectivity for data ingestion. ● Provide scalable, performant kubernetes environments both on-premises and in the cloud. ● Optimize cloud storage costs for growing media volumes. ● Design AI-powered detection of harmful content. ● Ensure that AI systems are auditable and their decisions can be explained ● Leverage LLMs and conversational AI for personalized experiences and content virality. ● Develop advanced chatbots with natural language understanding to provide personalized assistance. ● Automated summarization for diverse media. Executive Statement At Altostrat, we are embracing the next frontier of artificial intelligence to revolutionize our content strategy. By harnessing the power of generative AI, we will create an unparalleled user experience by empowering our audience with intelligent tools for content discovery, personalized recommendations, and seamless interaction. Reliability and cost management are our top priorities. This strategic initiative will deepen engagement, foster customer loyalty, and unlock new revenue streams through targeted marketing and tailored content offerings. We see a future where AI-driven innovation is central to our business, leading to greater success for our company and delivering exceptional value to our customers. ------------------------------------------------------------ Query The Altostrat Media data team has noticed that the performance of their recommendation engine has significantly decreased over the last month, despite no changes to the model code. They suspect that the distribution of incoming user data has changed compared to the data used during training. What is this phenomenon called, and how should it be addressed?

----Cymbal Retail Case Study---- Company Overview Cymbal is an online retailer experiencing significant growth. The retailer specializes in a large assortment of products spanning several retail sub-verticals, which makes managing their extensive product catalog a constant challenge. Solution Concept Cymbal wants to modernize its operations and enhance the customer experience in three core areas: ● Catalog and Content Enrichment: Cymbal wants to automate and improve the accuracy of their product catalog by utilizing gen AI to generate product attributes, descriptions, and images from supplier-provided information. This solution will streamline their catalog management, reduce manual effort and errors, and ensure information is consistent across all their sales channels. ● Conversational Commerce with Product Discovery: To enhance customer engagement and drive sales conversion, Cymbal wants to implement a Conversational Commerce solution. This solution will involve integrating AI- powered virtual agents into their website and mobile app to provide customers with a personalized and intuitive shopping experience through natural language conversations. These agents will utilize Google Cloud's Discovery AI to process user requests and retrieve the most relevant products based on each customer's needs and preferences, creating a more engaging and satisfying shopping journey. ● Technical Stack Modernization: To streamline operations and reduce costs around manual processes, data transfer, error handling and remediation, Cymbal wants to modernize their technical stack with cloud-based infrastructure, secure and efficient data handling, 3rd party integrations, and proactive monitoring and security. Existing Technical Environment Cymbal currently relies on the following environment: ● A mix of on-premises and cloud-based systems. ● A variety of databases, including MySQL, Microsoft SQL Server, Redis, and MongoDB, to store and manage its vast product catalog and customer data. ● Kubernetes clusters to run containerized applications. ● Legacy file-based integrations with on-premises systems, including SFTP file transfers, ETL batch processing. ● A custom-built web application which allows customers to browse the product catalog by querying the relational databases for names and categories of products. ● An IVR (Interactive Voice Response) system to handle initial customer calls and route them to the appropriate departments or agents. ● Call center agents who receive transferred calls from the IVR system and manually enter orders into the system when a customer can’t complete a transaction on their own. ● Various open source tools for monitoring such as Grafana, Nagios, and Elastic. The current technical environment has encountered significant challenges: manual processes are time-consuming and error-prone, data silos limit a unified view of the customer journey, and integrating new technologies is difficult. Business Requirements Cymbal has outlined these key business requirements for the gen AI solution: ● Automate Product Catalog Enrichment: Reduce manual effort, minimize errors, and ensure accuracy and consistency across the product catalog. ● Improve Product Discoverability: Enhance search relevance and enable customers to find products more efficiently. ● Increase Customer Engagement: Create a more interactive and personalized shopping experience to improve customer satisfaction and potentially reduce product returns. ● Drive Sales Conversion: Provide a more intuitive and helpful shopping experience to improve sales conversion rates and drive revenue growth. ● Reduce costs: Reduce call center staffing costs and data-center hosting costs. Technical Requirements ● Attribute Generation: Accurately derive relevant product attributes from various supplier data, including titles, descriptions, and images, ensuring the attributes align with the product category and Cymbal's existing catalog structure. ● Image Generation and Enhancement: Generate different product image variations from a base image (e.g., showcasing various colors). It should also support background changes, product color adjustments, and the addition of text overlays. ● Automate Product Discovery: Process customer requests expressed in natural language and return highly relevant product results. ● Scalability and Performance: The solution must handle Cymbal's extensive product catalog and accommodate their anticipated growth without compromising performance or user experience. ● Human-in-the-Loop (HITL) Review: Provide a user interface (UI) for associates to review and manage gen AI-generated content, allowing them to approve, reject, or modify suggestions before updating the product catalog. ● Data Security and Compliance: Ensure all customer data, including product information and interactions with virtual agents, are handled securely and comply with relevant industry regulations. Executive Statement By implementing Google Cloud's Generative AI for Digital Commerce solutions, Cymbal can transform its online retail operations to improve efficiency, enhance customer experience, and drive revenue growth. Key benefits for Cymbal include: ● Reduced operational costs through automation of catalog management tasks. ● Increased efficiency and speed in onboarding new products and updating existing ones. ● Improved accuracy and consistency of product information across all sales channels. ● A more engaging and personalized shopping experience that caters to modern customer preferences for conversational commerce. ● Enhanced product discoverability leading to higher conversion rates and increased sales. This strategic investment in generative AI will position Cymbal to remain competitive and thrive in the rapidly evolving landscape of online retail. ------------------------------------------------ Query Cymbal Retail currently runs some legacy inventory applications on-premises in their private data centers and some in Google Kubernetes Engine (GKE). They want to modernize their EKS (Amazon Elastic Kubernetes Service) clusters to ensure consistent policy management and security across all environments. Which solution is most appropriate? A) Migrate all EKS workloads to GKE Standard to eliminate multi-cloud overhead. B) Use Anthos to manage GKE, on-premises clusters, and EKS clusters through a single unified control plane. C) Deploy Model Garden containers directly onto EKS to handle Al inference locally. D) Use Bigtable replication to sync data between EKS and GKE.

The e-commerce application experiences a major traffic spike every Monday morning precisely at 9:00 AM, which often overwhelms the Compute Engine Managed Instance Group (MIG) before autoscaling can fully respond. How can the architect ensure the infrastructure is ready for the spike *proactively*?

A company is migrating a legacy application that relies on the NFS protocol for shared data access across multiple Linux servers. The data is accessed frequently and is mission-critical. The best Google Cloud storage service that provides a fully managed, scalable, and highly available equivalent to this on-premises file storage is: Cloud Filestore (Enterprise or High Scale) Cloud Bigtable Cloud Storage (Standard) Compute Engine Persistent Disk (Multi-Attach)

An application needs a key-value store with eventual consistency that can span multiple regions and is primarily used to cache or store user preferences that change infrequently. Which database should be selected for maximum availability and global distribution with eventual consistency?