An enterprise e-commerce platform hosted on Google Cloud requires an automated synthetic monitoring strategy to proactively detect performance regressions and functional failures before end users are impacted. The DevOps engineering team must satisfy the following distinct monitoring requirements: Requirement 1: Continuously probe the availability and response latency of public HTTP/HTTPS API gateway endpoints from multiple geographic regions with minimal operational overhead. Requirement 2: Simulate complex, client-side web application user journeys—including logging into user accounts, searching the catalog, adding items to a shopping cart, and rendering DOM elements. Requirement 3: Validate internal backend microservice API payloads and contract schemas within a secure environment using short-lived credentials rather than persistent service account key files. Which combination of synthetic monitoring architectures should the DevOps team implement?

Configure Cloud Monitoring Uptime Checks across global regions for public endpoints, deploy Mocha/Puppeteer-based Synthetic Monitors via Cloud Functions for browser user journeys, and deploy custom HTTP Synthetic Monitors using attached service account IAM identities for internal API contracts.: What the Solution Is This architecture combines native Cloud Monitoring Uptime Checks with Custom Synthetic Monitors implemented as Cloud Functions (2nd gen) leveraging the open-source Synthetic SDK (built on Mocha and Puppeteer) and IAM service account authentication. How It Meets the Requirements Requirement 1 (Public API Probing): Standard Cloud Monitoring Uptime Checks proactively send HTTP/HTTPS requests from multiple managed regional probe locations globally. They evaluate response codes and latency without requiring any compute infrastructure provisioning or ongoing maintenance. Requirement 2 (Multi-Step Browser Journeys): Synthetic Monitors running Puppeteer within Cloud Functions spin up a headless Chromium browser instance. The monitor script interacts with client-side DOM elements, executes multi-step workflows (login, search, cart interactions), and captures execution screenshots and trace metrics. Requirement 3 (Secure API Validation): Custom synthetic monitors implemented in Cloud Functions execute structured HTTP request suites to validate API JSON schemas. Because Cloud Functions run under an attached IAM service account identity, calls use ephemeral, short-lived access tokens natively acquired via the metadata server, eliminating the operational and security hazards of persistent service account key files. Key Advantages and Capabilities Managed Global Fleet: Cloud Monitoring automatically distributes uptime probes across multiple worldwide checker locations. Full Client-Side Emulation: Puppeteer automation evaluates dynamic Single Page Applications (SPAs) and JavaScript-rendered workflows. Zero Key Management: Utilizing runtime service account identities follows Google Cloud credential security best practices. Granular Observability: Test failures automatically emit metrics, logs, and trace telemetry directly to Cloud Monitoring and Cloud Logging. Why This Is Optimal This design aligns precisely with Google Cloud observability best practices by pairing lightweight managed probes for simple ping/status checks with programmable serverless execution environments for complex synthetic transactions.

Architect Synthetic Monitors Uptime Browser Checks | Professional Cloud DevOps Engineer

AdvancedProfessional Cloud DevOps Engineer

A DevOps team manages synthetic monitors in Cloud Monitoring that execute simulated multi-step user workflows against a web application every 30 seconds from multiple regions. Telemetry reveals a 99.9% overall success rate, but the latency distribution exhibits a distinct bimodal pattern where the primary mode finishes in under 80 ms while a secondary mode clusters between 450 ms and 550 ms. Additionally, the synthetic probes are generating significant compute execution costs and adding unnecessary load to backend database dependencies. Which strategy should the team implement to diagnose the root cause and optimize the synthetic monitoring configuration?

Creating synthetic monitors to proactively probe application endpoints and workflows

AdvancedProfessional Cloud DevOps Engineer

Your organization operates a mission-critical, multi-region checkout API hosted on Google Cloud. You need to establish synthetic monitoring to proactively detect customer-impacting issues before real traffic is disrupted. Your monitoring strategy must fulfill the following operational requirements: Probe the checkout endpoint from multiple global geographic regions at a 1-minute frequency. Validate that the endpoint responds with an HTTP 200 OK, contains a specific JSON confirmation key in the response body, and passes TLS certificate validity verification. Generate an incident and notify on-call engineers via Slack and PagerDuty if the 99th percentile (p99) latency exceeds 250 ms across probes for a 5-minute rolling window. Which combination of actions should you configure in Google Cloud Monitoring to meet these requirements?

Creating synthetic monitors to proactively probe application endpoints and workflows

AdvancedProfessional Cloud DevOps Engineer

An organization runs fault-tolerant batch data processing and CI/CD build tasks on Google Cloud Compute Engine. The DevOps team needs to optimize compute expenses using Spot VMs while satisfying the following operational criteria: The fleet must use the Spot VM provisioning model to achieve up to 91% cost savings. The instances must gracefully handle preemption events to persist execution state and progress. The infrastructure must automatically recreate and resume instances when Spot capacity becomes available without manual intervention. The instance configuration must adhere to Google Cloud platform restrictions for Spot VMs. Which configuration strategy should the DevOps team implement?

Spot virtual machines (VMs)

AdvancedProfessional Cloud DevOps Engineer

Your organization is designing an enterprise log export and retention pipeline in Google Cloud to meet strict financial regulatory standards. The pipeline architecture has the following compliance and operational requirements: Route all organization-wide audit logs to an immutable Cloud Storage destination where records cannot be modified or deleted by any user or administrator for seven years. Export application logs to BigQuery for real-time SQL analytics, optimizing query costs and enabling automated expiration of data older than 90 days. Follow the principle of least privilege when configuring access and authorization for the log export mechanisms. Which combination of actions should you implement?

Exporting and retaining logs (e.g., routing to BigQuery, Pub/Sub, Cloud Storage)

AdvancedProfessional Cloud DevOps Engineer

Your organization operates multiple Google Kubernetes Engine (GKE) clusters across staging and production environments. All production clusters are enrolled in the Regular release channel to ensure baseline operational stability. Due to strict corporate security policies, your team must ensure that newly published security patches are automatically applied to the control plane and nodes as quickly as possible without waiting for GKE's multi-week qualification period, while still strictly honoring configured maintenance windows and exclusions. Which configuration should you apply to meet these requirements?

Safe and secure patching and upgrading practices

IntermediateProfessional Cloud Architect

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