'gcloud' 검색 결과

Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enable automatic scaling for GKE node pools
• Find why nodes do not scale up
• Identify constraints that prevent nodes from scaling down

Design production GKE clusters, which run containerized applications on Google Cloud, using recommended Autopilot settings. Review networking, security, monitoring, and upgrade choices, then compare existing clusters against the configuration and identify deviations and remedies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a production GKE cluster
• Review network choices before cluster creation
• Audit cluster settings against the recommended configuration

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

Configure how GKE applications receive traffic from the internet or internal networks. The skill provides configuration templates for routing, load balancing, certificates, and access protection, and guides diagnosis of failed health checks, dropped requests, and TLS connection problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Expose a GKE application through Gateway API or Ingress
• Split traffic between application versions during a rollout
• Configure Cloud Armor protection and HTTPS certificates

Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying an existing application to GKE for the first time
• Creating a Dockerfile and registering a container image
• Preparing deployment configuration with health checks

Plan and configure a shared GKE cluster for multiple teams or environments. Set up namespaces, team permissions, resource limits, and network separation, then use labels and GKE cost allocation to track spending by team.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a GKE cluster shared by multiple teams
• Separate development, staging, and production workloads
• Set team permissions and resource quotas

Inspect GKE TPU Dynamic Slices, diagnose provisioning failures, and validate workload configuration for single slices or JobSet workloads spanning multiple slices. Get cleanup commands, with confirmation required before destructive changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking TPU slice lifecycle states
• Diagnosing TPU slice provisioning failures
• Validating single-slice and multi-slice JobSet manifests

Set up a managed Kubernetes environment on Google Cloud, from choosing Autopilot or Standard to creating a GKE cluster, obtaining kubectl connection credentials, and deploying workloads. Includes guidance on private clusters and resource requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing between Autopilot and Standard
• Creating a private GKE cluster
• Obtaining kubectl credentials for a cluster

Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate vbar_control_agent crashes and memory exhaustion
• Check tpu-device-plugin metrics checksum errors
• Inspect conflicts with custom TPU metrics collectors

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating unknown metrics and HPA errors
• Diagnosing workloads that will not scale up or down
• Troubleshooting scaling to and from zero

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert Cloud Monitoring metric definitions into PromQL
• Filter monitoring queries to specific resources
• Choose aggregations that match metric types

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Moving a Gemini application to Google Cloud
• Using Google Cloud credits for Gemini API calls
• Unifying agent authentication and billing

Set up credentials and a development environment for your first Google Ads API request to retrieve campaigns. Choose from six official client libraries or direct HTTP requests, with guidance for account IDs, developer tokens, and common authorization errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare credentials for a first Google Ads API integration
• Build an example that retrieves advertising campaigns
• Choose a client library or direct REST requests

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

Prepare a local development environment for the Data Manager API with authentication and library installation guidance. It covers five programming languages and companion utilities for formatting, hashing, and encrypting user identifiers before ingestion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a local environment for the Data Manager API
• Installing client and identifier utility libraries
• Configuring local credentials and access scopes

Prepare to fine-tune open models or Gemini models on Agent Platform. Check the environment, convert and validate training data, choose supported models and settings, and estimate tuning costs for open models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether an Agent Platform tuning environment is ready
• Converting and validating training datasets
• Choosing models and training settings for a task

Manage machine learning models and their versions in the Agent Platform Model Registry. List registered models, inspect details, upload new versions, update names or descriptions, and delete models through workflows that require confirmation for changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• List registered models and inspect their details
• Register a new model or model version
• Update a model's display name or description

Create and administer AlloyDB for PostgreSQL databases, including clusters, instances, backups, secure connections, and access permissions. It also covers command-line operations, infrastructure configuration, and MCP tools that let agents automate database tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an AlloyDB cluster and primary instance
• Manage backups and plan recovery
• Configure secure connections and access permissions

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collect logs from multiple projects in one central bucket
• Query distributed logs without copying their data
• Set log access boundaries by project or log type

Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings