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.

Evaluate Active Assist Recommender Finops Prioritization

AdvancedProfessional Cloud DevOps Engineer

An enterprise DevOps team is designing an automated workflow to ingest and process Google Cloud Recommender suggestions across multiple production projects within their GitOps CI/CD pipeline. The governance policy defines the following criteria: Low-risk operating system and runtime compliance baselines must be automatically remediated and enforced. High-risk security modifications (such as reducing over-privileged permissions identified by IAM Recommender or changing Cloud Service Mesh authorization policies) must be classified for human review via peer-reviewed pull requests rather than being applied directly. The CI/CD pipeline service account must follow the principle of least privilege for recommendation discovery. Which workflow and configuration should the team implement?

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

AdvancedProfessional Cloud DevOps Engineer

A DevOps team manages multi-project CI/CD pipelines and infrastructure deployments across an enterprise Google Cloud organization. To meet strict regulatory compliance and security auditing standards, the team must implement a centralized deployment auditing strategy that satisfies the following requirements: Track all administrative resource modifications (such as modifications to deployment pipelines and cluster configurations) across all current and future projects. Capture read and write operations targeting sensitive deployment assets (such as Kubernetes Secrets accessed via the Kubernetes API server). Retain all audit data centrally for multi-year compliance analysis and allow security analysts to perform complex analytical SQL queries across the unified dataset. Which combination of Cloud Audit Logs configuration and log routing should the DevOps engineer implement?

Auditing and tracking deployments (e.g., Artifact Registry, Cloud Build, Cloud Deploy, Cloud Audit Logs)

AdvancedProfessional Cloud DevOps Engineer

Your team is building an automated continuous training (CT) and continuous deployment (CD) pipeline for a mission-critical tabular classification model on Google Cloud. You need to implement a solution using Vertex AI Pipelines that continuously evaluates candidate models and deploys them to production. The pipeline implementation must meet the following operational requirements: Track end-to-end dataset lineage, hyperparameter configurations, and model artifact provenance in Vertex ML Metadata. Execute automated model evaluation on validation test data and compute classification metrics. Use conditional execution logic within the pipeline directed acyclic graph (DAG) to trigger deployment to the production endpoint only if candidate model metrics exceed a target accuracy threshold. Which architecture and pipeline design should you implement?

Deployment strategies (e.g., canary, blue/green, rolling, traffic splitting, feature flags) and defining success metrics based on application or ML pipeline telemetry

AdvancedProfessional Cloud DevOps Engineer

An e-commerce platform running on Google Cloud experienced an unexpected latency spike and intermittent 500 error responses between 14:00 and 14:45 UTC. The architecture consists of a fleet of Compute Engine instances serving front-end traffic and a backend Cloud SQL instance handling transactional queries. You are using the Gemini Cloud Assist panel in the Google Cloud console to perform an investigation across both resource tiers. Which natural language prompt is most effective for directing Gemini Cloud Assist to correlate time-series metrics and generate actionable root-cause hypotheses for the incident?

Leveraging Gemini Cloud Assist for metrics interpretation

AdvancedProfessional Cloud DevOps Engineer

Your organization operates a global, mission-critical application hosted on Google Cloud using Compute Engine Managed Instance Groups (MIGs) behind an external Application Load Balancer. To improve release velocity and prevent customer-facing incidents, the SRE team requires the deployment pipeline to automate canary deployments, metric validation, and traffic draining. The automated CI/CD workflow must satisfy the following criteria: Route a small percentage (5%) of live user requests to a canary backend group. Run automated canary analysis for 15 minutes by evaluating Cloud Monitoring metrics and health checks against defined Service Level Objectives (SLOs). Immediately redirect traffic away from the canary and gracefully drain active requests without dropping connections if an anomaly is detected. Automatically promote the canary to 100% traffic and orchestrate graceful connection draining on the retiring legacy backend MIG before deprovisioning. Which strategy should you integrate into your CI/CD pipeline to orchestrate this automated traffic draining and canary workflow?

Draining/redirecting traffic

FundamentalAssociate Data Practitioner

SQL GROUP BY with Aggregate Functions

Analyze data to answer business questions

Professional Cloud DevOps Engineer

Evaluate Active Assist Recommender Finops Prioritization

Related questions

SQL GROUP BY with Aggregate Functions

Professional Cloud DevOps Engineer

Evaluate Active Assist Recommender Finops Prioritization

What the Solution Is

How It Meets the Requirements

Key Advantages

Why This Is Optimal

Related questions

SQL GROUP BY with Aggregate Functions

What the Solution Is

How It Meets the Requirements

Key Advantages

Why This Is Optimal