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Chapter 1.
Designing the overall resource hierarchy for an organization. Considerations include:
- Projects and folders
- Shared networking
- Identity and Access Management (IAM) roles and organization-level policies
- Creating and managing service accounts
- Infrastructure as code tooling (e.g., Cloud Foundation Toolkit, Config Connector, Terraform, Helm)
- Making infrastructure changes using Google-recommended practices and infrastructure as code blueprints
- Immutable architecture
Designing a CI/CD architecture stack in Google Cloud, hybrid, and multi-cloud environments. Considerations include:
- CI with Cloud Build
- CD with Google Cloud Deploy
- Widely used third-party tooling (e.g., Jenkins, Git, ArgoCD, Packer)
- Security of CI/CD tooling
Chapter 2.
2.0 Building and implementing CI/CD pipelines for a service
2.1 Design CI/CD pipelines
- Artifact management with Artifact Registry
- Deployment to hybrid and multi-cloud environments (e.g., Anthos, GKE)
- CI/CD pipeline triggers
- Testing a new application version in the pipeline
- Auditing and tracking deployments (e.g., Artifact Registry, Cloud Build, Google Cloud Deploy, Cloud Audit Logs)
- Deployment strategies (e.g., canary, blue/green, rolling, traffic splitting)
- Rollback strategies
- Troubleshooting deployment issues
- Secure storage methods and key rotation services (e.g., Cloud Key Management Service, Secret Manager)
- Secret management
- Build versus runtime secret injection
- Vulnerability analysis with Artifact Registry
- Binary Authorization
- IAM policies per environment
Chapter 3.
3.0 Implementing service monitoring strategies
3.1 Manage application logs
3.2 Manage application metrics with Stackdriver Monitoring
3.3 Manage Stackdriver Monitoring platform
3.4 Manage Stackdriver Logging platform
3.5 Implement logging and monitoring access controls
- Discovering SLIs (e.g., availability, latency)
- Defining SLOs and understanding SLAs
- Error budgets
- Toil automation
- Opportunity cost of risk and reliability (e.g., number of “nines”)
- Service management (e.g., introduction of a new service by using pre-mortems [pre-service onboarding checklist, launch plan, or deployment plan], deployment, maintenance, and retirement)
- Capacity planning (e.g., quotas and limits management)
- Autoscaling using managed instance groups, Cloud Run, Cloud Functions, or GKE
- Implementing feedback loops to improve a service
- Preventing burnout (e.g., setting up automation processes to prevent burnout)
- Fostering a culture of learning and blamelessness
- Establishing joint ownership of services to eliminate team silos
Chapter 4.
4.0 Optimizing service performance
4.1 Identify service performance issues
4.2 Debug application code
4.3 Optimize resource utilization
- Communicating during an incident
- Draining/redirecting traffic
- Adding capacity
Chapter 5.
5.0 Managing service incidents
5.1 Coordinate roles and implement communication channels during a service incident
5.2 Investigate incident symptoms impacting users with Stackdriver IRM
5.3 Mitigate incident impact on users
5.4 Resolve issues
5.5 Document issue in a postmortem
- Collecting structured and unstructured logs from Compute Engine, GKE, and serverless platforms using Cloud Logging
- Configuring the Cloud Logging agent
- Collecting logs from outside Google Cloud
- Sending application logs directly to the Cloud Logging API
- Log levels (e.g., info, error, debug, fatal)
- Optimizing logs (e.g., multiline logging, exceptions, size, cost)
- Collecting and analyzing application and platform metrics
- Collecting networking and service mesh metrics
- Using Metrics Explorer for ad hoc metric analysis
- Creating custom metrics from logs
- Creating a monitoring dashboard
- Filtering and sharing dashboards
- Configuring alerting
- Defining alerting policies based on SLOs and SLIs
- Automating alerting policy definition using Terraform
- Using Google Cloud Managed Service for Prometheus to collect metrics and set up monitoring and alerting
- Enabling data access logs (e.g., Cloud Audit Logs)
- Enabling VPC Flow Logs
- Viewing logs in the Google Cloud console
- Using basic versus advanced log filters
- Logs exclusion versus logs export
- Project-level versus organization-level export
- Managing and viewing log exports
- Sending logs to an external logging platform
- Filtering and redacting sensitive data (e.g., personally identifiable information [PII], protected health information [PHI])
- Using Google Cloud’s operations suite to identify cloud resource utilization
- Interpreting service mesh telemetry
- Troubleshooting issues with compute resources
- Troubleshooting deploy time and runtime issues with applications
- Troubleshooting network issues (e.g., VPC Flow Logs, firewall logs, latency, network details)
- Application instrumentation
- Cloud Logging
- Cloud Trace
- Error Reporting
- Cloud Profiler
- Cloud Monitoring
- Preemptible/Spot virtual machines (VMs)
- Committed-use discounts (e.g., flexible, resource-based)
- Sustained-use discounts
- Network tiers
- Sizing recommendations
