An enterprise is reviewing its monthly Google Cloud infrastructure spend to optimize Operational Expenditure (OpEx). An architectural audit reveals the following workload profiles: Production Web & Database Tier: Runs continuously (24/7) with steady, predictable CPU and memory demand across multiple regions. Batch Image Processing: Fault-tolerant, stateless processing jobs that can tolerate unexpected interruptions and node terminations. Legacy Application VMs: Individual virtual machines that have shown persistent CPU utilization below 10% and low memory consumption over the past several weeks. Which combination of optimization strategies should you implement to maximize cost savings while maintaining production reliability?

Apply machine type rightsizing recommendations to downsize underutilized legacy VMs, purchase Committed Use Discounts (CUDs) for baseline 24/7 production workloads, and provision Spot VMs for fault-tolerant batch processing.: What This Strategy Is This multi-tiered cost optimization strategy leverages Active Assist rightsizing recommendations, Committed Use Discounts (CUDs), and Spot Virtual Machines (Spot VMs) to target each workload profile according to its operational requirements and fault tolerance. How It Meets The Requirements Legacy Application VMs: Compute Engine generates machine type recommendations by analyzing historical CPU and memory utilization gathered by Cloud Monitoring. Resizing overprovisioned VMs eliminates wasted compute capacity without impacting performance. Production Tier: Purchasing Committed Use Discounts (CUDs) (for 1-year or 3-year terms) provides significant price reductions (up to 57% or more for general-purpose compute) in exchange for a commitment to continuous baseline resource or spend usage. Batch Processing: Deploying Spot VMs provides up to 60-91% discounts compared to standard on-demand pricing. Because the batch rendering pipeline is stateless and fault-tolerant, interruptions caused by Compute Engine reclaiming capacity do not risk data integrity. Key Advantages Maximizes OpEx efficiency: Combines discounts on commitment, spare capacity, and right-sized infrastructure for maximum total cost of ownership (TCO) reduction. Preserves high availability: Ensures critical 24/7 production services remain on stable, non-preemptible resources backed by contract discounts. Automates optimization insights: Utilizes Google Cloud's built-in metric analysis to continuously identify overprovisioned instances. Why This Is Optimal Aligning discounting mechanisms to workload characteristics ensures that you do not overpay for guaranteed availability where it is unnecessary (the batch tier), while securing the highest predictable savings on continuous baseline architectures.

Compute Resource Cost Optimization Cuds Spot Rightsizing

IntermediateProfessional Cloud Architect

An enterprise is planning to migrate its core application workloads from an on-premises data center to Google Cloud. In their current on-premises environment, the company incurs large upfront capital expenditures (CapEx) to procure hardware sized for peak annual traffic, resulting in low average hardware utilization throughout most of the year. The leadership team wants to understand the financial implications of moving to a cloud consumption model and how this shift affects Total Cost of Ownership (TCO) and organizational financial agility. Which statement accurately describes the economic impact of transitioning from fixed-cost infrastructure to a cloud variable-cost structure?

Cost optimization/resource optimization (CapEx/OpEx)

IntermediateProfessional Cloud Architect

An enterprise is migrating multiple backend workloads to Google Cloud and must map each workload to the optimal compute service based on operational overhead, scaling characteristics, and architectural dependencies: Workload 1: A containerized stateless web API that receives fluctuating HTTP(S) request traffic, requires rapid automatic scaling (including scaling to zero when idle), and must minimize cluster management overhead. Workload 2: A lightweight, single-purpose event handler that executes short-lived business logic asynchronously whenever new files are uploaded to a Cloud Storage bucket. Workload 3: A complex distributed application that requires deployment via Helm, interacts directly with Kubernetes Custom Resource Definitions (CRDs), and enforces fine-grained multi-tenant isolation using Kubernetes Role-Based Access Control (RBAC). Which compute mapping should you recommend?

Mapping compute needs to platform products (e.g., Google Kubernetes Engine [GKE], Cloud Run, and Cloud Run functions)

IntermediateProfessional Cloud Architect

Separation of Duties with Resource Hierarchy and IAM

Separation of duties

IntermediateProfessional Cloud Architect

An e-commerce organization hosts a latency-sensitive public web service on Google Cloud using App Engine standard environment with automatic scaling. The service currently scales down to zero instances during inactive overnight periods to save costs. However, during sudden morning traffic spikes, users experience latency degradation caused by instance cold starts and request queuing before new instances can spin up. You need to optimize the autoscaling behavior to satisfy the following requirements: Eliminate cold-start latency for initial incoming morning requests Proactively scale out new instances based on concurrent connections before current instances reach full capacity Retain automatic scaling capabilities during high-traffic periods Which configuration should you specify in the application's automatic scaling configuration?

Serverless computing

IntermediateProfessional Cloud Architect

Private Google Access and Cloud NAT

VPC design and load balancing (e.g., access to cloud, internet, and cloud adjacent services)

AdvancedProfessional Cloud Security Engineer

An enterprise organization is designing a centralized logging, threat detection, and security analytics architecture across Google Cloud and hybrid environments. The Security Operations Center (SOC) team needs to achieve the following requirements: Ingest and normalize diverse log sources (including cloud audit logs, web proxy logs, and endpoint telemetry) into a standard schema for fast threat hunting and multi-event correlation. Write custom behavioral detection rules that correlate disparate entities (such as IP addresses, users, and processes) across time windows. Integrate native Google Cloud threat intelligence findings alongside third-party telemetry in a single security analytics repository. Which logging and threat detection strategy should the organization implement?

Designing an effective logging strategy.

Professional Cloud Architect

Compute Resource Cost Optimization Cuds Spot Rightsizing

Related questions

Separation of Duties with Resource Hierarchy and IAM

Private Google Access and Cloud NAT

Professional Cloud Architect

Compute Resource Cost Optimization Cuds Spot Rightsizing

What This Strategy Is

How It Meets The Requirements

Key Advantages

Why This Is Optimal

Related questions

Separation of Duties with Resource Hierarchy and IAM

Private Google Access and Cloud NAT

What This Strategy Is

How It Meets The Requirements

Key Advantages

Why This Is Optimal