An enterprise organization recently migrated several business units to Google Cloud. A customer success architect reviews the environment and notices that adoption for a critical shared analytics service has stalled because users are experiencing latency spikes and intermittent performance degradation during peak hours. At the same time, overall cloud spending is exceeding forecasts due to over-provisioned, idle compute resources in other projects. You need to implement a proactive health and optimization strategy that identifies technical barriers to platform adoption, reduces risk, and optimizes infrastructure cost and performance. Which approach should you recommend?

Use Cloud Monitoring and BigQuery billing exports to analyze performance metrics and spending trends, review Active Assist recommendations for right-sizing, and implement autoscaling policies alongside load-balancer health checks.: What the Unified Observability and Active Assist Strategy Is This strategy combines continuous observability through Cloud Monitoring, granular financial analysis using BigQuery billing exports, automated intelligent insights from Active Assist / Recommender, and dynamic scaling paired with health checks to maintain optimal workload availability and cost efficiency. How It Meets the Requirements Identifies Technical Barriers to Adoption: By tracking key performance indicators such as latency, throughput, and error rates in Cloud Monitoring, platform operators can pinpoint the exact service bottlenecks causing user dissatisfaction and poor adoption. Optimizes Cost and Right-Sizes Resources: Exporting billing data to BigQuery allows multi-dimensional cost queries by label and project. Coupled with Active Assist, the platform automatically generates actionable right-sizing recommendations for over-provisioned or idle virtual machines. Proactively Mitigates Risks: Configuring load balancer health checks ensures that degraded or unhealthy instances are automatically taken out of service and autohealed, while dynamic autoscaling adjusts compute capacity automatically during peak usage periods to prevent performance dips. Key Advantages and Capabilities Automated Intelligence: Employs machine learning via Active Assist to identify idle resources and suboptimal configurations without manual auditing. Data-Driven Decision Making: Merges telemetry data and cost data to visualize performance against key business objectives. Elastic Scaling: Automatically handles traffic spikes during peak hours using Managed Instance Groups (MIGs) or GKE Horizontal Pod Autoscaling (HPA). Proactive Autohealing: Continuously evaluates service readiness and liveness to maintain high availability. Why This Is Optimal This solution addresses both the operational health bottlenecks preventing user adoption and the financial inefficiencies driving cloud spend overruns. It leverages native Google Cloud services designed specifically for continuous optimization, automated scaling, and proactive risk mitigation.

Monitor Platform Adoption Health Resource Optimization

IntermediateProfessional Cloud Architect

An enterprise is expanding its mission-critical production workloads on Google Cloud under an Enhanced Support plan. The company's executive leadership wants to establish strategic governance and partnership mechanisms to ensure their cloud infrastructure evolves alongside changing business priorities. Specifically, the enterprise requires guided operational onboarding, proactive guidance to optimize and enable cloud strategy and operations, and best practices for managing and escalating their most critical support cases. Which Google Cloud Customer Care capability should the organization adopt to satisfy these requirements?

Customer success management

IntermediateProfessional Cloud Architect

An enterprise has migrated multiple line-of-business applications onto shared, multi-tenant Google Kubernetes Engine (GKE) clusters. Executive leadership requires the cloud architecture team to establish a value realization framework to evaluate return on investment (ROI) and platform adoption. The framework must satisfy the following business requirements: Accurately attribute cloud resource consumption and compute costs to specific business units and application teams. Compare requested resource allocations against actual workload utilization to identify over-provisioning and financial waste. Provide business stakeholders with customizable visual dashboards for long-term tracking of key performance indicators (KPIs). Which solution should the cloud architect implement to track these value realization metrics?

Customer success management

IntermediateProfessional Cloud Architect

Fleets, Config Sync, Policy Controller, Cloud Service Mesh

Integration with on-premises/multicloud environments

IntermediateProfessional Cloud Architect

Transfer Appliance vs STS vs BigQuery DTS

Data and system migration and management tooling

IntermediateProfessional Cloud Architect

An e-commerce retailer is migrating its core digital storefront and checkout application to Google Cloud. Executive leadership wants to establish an observability and performance measurement framework that directly bridges technical infrastructure metrics with business outcomes, such as transaction completion rates, user engagement, and revenue protection. Which strategy should the cloud architect implement to align technical key performance indicators (KPIs) with organizational business goals?

Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], and metrics)

AdvancedProfessional Cloud DevOps Engineer

Your DevOps team deploys a critical microservice on Compute Engine instances with the Google Cloud Ops Agent. The Ops Agent is configured to ingest OpenTelemetry Protocol (OTLP) metrics using the Cloud Monitoring API mode (metrics_mode: googlecloudmonitoring). The application emits two cumulative counter metrics: workload.googleapis.com/custom/http_requests_total workload.googleapis.com/custom/http_errors_total You need to construct a PromQL-based alerting policy condition in Cloud Monitoring that triggers an incident whenever the 5-minute HTTP error rate exceeds 5% of total requests across the fleet. Which PromQL query correctly references these metrics according to Cloud Monitoring syntax conventions?

Creating custom metrics, including log-based metrics

Professional Cloud Architect

Monitor Platform Adoption Health Resource Optimization

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