Pass the Google Cloud Certified Professional-Cloud-Architect Questions and answers with Dumpstech

According to Google Cloud Hybrid Connectivity documentation, Cloud VPN is the standard and recommended solution for establishing a secure, encrypted connection over the public internet between an on-premises network and a Google Cloud VPC. This aligns with Cymbal’s requirement for " secure communication " while maintaining manageability. Unlike SSH tunnels (Option B), which are fragile and difficult to scale at an enterprise level, Cloud VPN utilizes IPsec protocols to create a stable and encrypted site-to-site tunnel.

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To satisfy the " secure and manageable " criteria, the implementation must include strictly defined VPC Firewall Rules . This ensures the principle of least privilege is applied, restricting on-premises systems to only the necessary cloud resources and ports required for their specific business functions. VPC Peering (Option D) is technically incorrect here as it is designed for connecting two VPCs within the cloud, not for on-premises to cloud connectivity. A Bastion Host (Option A) provides a secure gateway for administrative login (SSH/RDP) but does not provide the transparent, network-level connectivity required for the automated " legacy file-based integrations " and " data transfers " mentioned in Cymbal’s environment. By using Cloud VPN with granular firewalling, Cymbal achieves a secure extension of their data center into Google Cloud.

According to the Google Cloud Architecture Framework: Performance Excellence , Local SSDs are the optimal choice for " scratch " space, temporary files, and workloads requiring the lowest possible latency and highest IOPS. Unlike Persistent Disks (Option C) or Filestore (Option B), which are network-attached, Local SSDs are physically attached to the server that hosts the virtual machine instance.

This physical proximity eliminates network latency, providing sub-millisecond access times which are critical for " frequently accessed and modified " temporary files during AI training and inference. For generative AI, where data shuffling and checkpointing are intensive, the high throughput of Local SSDs prevents the storage layer from becoming a bottleneck for the expensive GPU/TPU accelerators.

While the data on Local SSDs is ephemeral (lost if the VM is deleted or undergoes certain maintenance events), this perfectly matches Cymbal ' s requirement for " temporary files. " Furthermore, Local SSDs can be more cost-effective than provisioning high-performance Persistent Disks of equivalent speed, as their performance is not tied to the disk size. Cloud Storage (Option A) is unsuitable for this specific use case due to the significantly higher latency of object-based storage compared to block-based local storage.

According to the Google Cloud Architecture Framework for modernizing applications, Google Kubernetes Engine (GKE) is the most suitable compute platform for services requiring high scalability and availability. Cymbal’s AI-powered virtual agents must handle " anticipated growth " and fluctuating customer demands from their web and mobile apps. GKE’s Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler ensure that the compute resources expand automatically during peak shopping hours and shrink during low-traffic periods to " minimize costs. "

GKE is explicitly mentioned in Cymbal’s " Existing Technical Environment " as their preferred platform for containerized applications. Leveraging GKE for the virtual agents allows the development team to use a microservices approach, isolating the agent ' s logic from other catalog enrichment tasks. Option D (Single large VM) is a " legacy " approach that creates a single point of failure and does not scale efficiently. Option A (Cloud SQL) is a database service, not a compute resource for running agent logic. While Vertex AI Agent Builder (Option B) is the tool used to create the agents, the underlying compute that hosts the integrated application and handles the traffic orchestration is best served by a managed Kubernetes environment like GKE, which aligns with their requirement for " Scalability and Performance. "

According to Google Cloud Storage documentation , the soft-delete policy is a feature enabled by default on new buckets (starting in 2024) to protect against accidental or malicious deletion. It retains deleted objects for a specified retention period (default is seven days). For workloads involving large files—such as the video files Cymbal uses for Vertex AI training—this policy can lead to unexpected cost spikes.

When soft-delete is active, objects that are deleted are not immediately removed from the billing cycle; instead, they continue to incur charges at the same rate as " live " objects until the retention period expires. In an AI training environment where datasets are frequently updated, replaced, or temporary " scratch " files are created and deleted, the volume of soft-deleted bytes can quickly double or triple the effective storage footprint.

Options B and D are incorrect because moving from dual-region or multi-region to a single region would actually reduce storage costs, as regional storage is priced lower. Option A is incorrect because disabling the policy would stop the retention of deleted bytes, thereby lowering the bill. Therefore, checking for an active or recently enabled soft-delete policy is the standard troubleshooting step for sudden increases in storage costs associated with high-churn, large-file datasets.

According to the Google Cloud Architecture Framework and Google Cloud Armor documentation , Google Cloud Armor is the enterprise-grade solution for protecting applications and services against Distributed Denial of Service (DDoS) attacks and web-based exploits. Altostrat requires a solution that addresses " multi-vector " attacks at " various layers, " specifically the Network/Transport layers ( L3/L4 ) and the Application layer ( L7 ).

Google Cloud Armor provides built-in, always-on protection against L3 and L4 volumetric attacks at the edge of Google’s network, preventing malicious traffic from reaching the backend GKE clusters or Cloud Run functions. For L7 (Application Layer) protection, Cloud Armor offers Web Application Firewall (WAF) capabilities, including pre-configured rule sets (based on OWASP Top 10 ), rate limiting, and Adaptive Protection . The latter uses machine learning to detect anomalies in traffic patterns specific to Altostrat’s applications, automatically suggesting rules to block sophisticated botnets or application-layer floods.

While Cloud NGFW (Option B) provides essential network filtering, it lacks the native L7 inspection and global DDoS scrubbing capabilities inherent to Cloud Armor. VPC Service Controls (Option A) focus on data exfiltration and API security perimeters, and Security Command Center (Option D) is a posture management and threat detection tool rather than an active traffic mitigation service. Deploying Cloud Armor at the global load balancing tier ensures Altostrat maintains the high availability and reliability required for its global media streaming audience.

https://cloud.google.com/security/compliance/hipaa

https://cloud.google.com/kubernetes-engine/d ocs/concepts/private-cluster-concept#overview

https://cloud.google.com /pubsub/docs/publisher?hl=en#batching

https://cloud.google.com/compute/docs/ip-addresses/reserve-static-external-ip-address#disableexternalip