A DevOps engineer is reviewing Active Assist recommendations across an enterprise Google Cloud environment to optimize operational costs and resource utilization. The recommender hub has generated multiple insights across compute, networking, and disaster recovery architectures: Item 1: Downsize an over-provisioned MySQL database VM that has high memory usage for its InnoDB buffer pool cache but consistently low vCPU utilization by switching from a high-core standard shape to a highmem instance type. Item 2: Migrate multi-region Kafka Connect streaming pipelines to single-region deployments without evaluating cross-region source/sink data gravity. Item 3: Replace regional Application Load Balancer traffic routing with centralized hub-and-spoke Layer 7 firewall appliances across all spoke VPCs to consolidate egress egress paths. Item 4: Immediately deprovision stopped Compute Engine disaster recovery VM instances and detached boot disks in the secondary recovery region. Which recommendation represents the highest business impact and technical feasibility for immediate FinOps cost reduction while minimizing operational risk?

Item 1: Rightsizing the MySQL database VM to a highmem machine type to match the active buffer pool working set while reducing unneeded vCPUs.: ### What the Solution Is **Active Assist VM rightsizing recommendations** identify instances where compute allocations do not match actual runtime metrics, allowing organizations to reduce virtual CPU (**vCPU**) allocation while retaining sufficient memory for cache-dependent workloads. ### How It Meets the Requirements - **High Financial Impact**: Database workloads frequently require substantial memory for caching hot data (such as the **MySQL InnoDB buffer pool**), but require significantly less raw processor capacity for baseline queries. Switching to a `highmem` shape (offering ~8 GB RAM per vCPU) eliminates waste from unutilized vCPUs while protecting database performance. - **Technical Feasibility**: Modifying a machine type in **Compute Engine** is a straightforward, low-risk operational procedure that requires a simple scheduled instance stop, configuration update, and restart, without rewriting application architecture or re-engineering network paths. ### Key Advantages - **Direct Cost Optimization**: Eliminates recurring billing for unused vCPU compute capacity immediately. - **Preserved Workload Throughput**: Maintains the required memory footprint so the working set stays resident in RAM to prevent disk I/O thrashing. - **Low Implementation Complexity**: Machine type changes can be automated through Terraform, Cloud SDK (`gcloud`), or standard maintenance windows. ### Why This Is Optimal Compared to complex network topology shifts (Item 2 and Item 3) or compromising disaster recovery readiness (Item 4), rightsizing compute resources based on working set analysis delivers immediate ROI with minimal operational friction.

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Evaluate Active Assist Recommender Finops Prioritization

Section: Leveraging Google Cloud recommenders (e.g., cost, security, performance, manageability, reliability)

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A DevOps engineer is reviewing Active Assist recommendations across an enterprise Google Cloud environment to optimize operational costs and resource utilization. The recommender hub has generated multiple insights across compute, networking, and disaster recovery architectures:

Item 1: Downsize an over-provisioned MySQL database VM that has high memory usage for its InnoDB buffer pool cache but consistently low vCPU utilization by switching from a high-core standard shape to a highmem instance type.
Item 2: Migrate multi-region Kafka Connect streaming pipelines to single-region deployments without evaluating cross-region source/sink data gravity.
Item 3: Replace regional Application Load Balancer traffic routing with centralized hub-and-spoke Layer 7 firewall appliances across all spoke VPCs to consolidate egress egress paths.
Item 4: Immediately deprovision stopped Compute Engine disaster recovery VM instances and detached boot disks in the secondary recovery region.

Which recommendation represents the highest business impact and technical feasibility for immediate FinOps cost reduction while minimizing operational risk?

Professional Cloud DevOps Engineer

Evaluate Active Assist Recommender Finops Prioritization

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