GCP

Practice mapping app jobs to GCP projects, APIs, resources, callers, billing, and shared responsibility.

Practice placing a GCP workload through projects, folders, billing accounts, APIs, quotas, regions, and zones.

Practice using project IDs, names, labels, tags, and resource paths to identify exact GCP resources before changing them.

Practice mapping GCP service families to app jobs for traffic, compute, state, access, signals, deployment, cost, and recovery.

Practice reading GCP IAM as principal, role, resource, scope, condition, and evidence before applying fixes.

Practice runtime identity, deploy identity, ADC, impersonation, keys, and Workload Identity Federation for GCP workloads.

Practice secret names, versions, IAM access, runtime flow, rotation, evidence, encryption, and KMS judgment.

Practice separating global VPC networks, regional subnets, route decisions, and service-specific attachment points.

Practice GCP firewall direction, priority, targets, sources, implied rules, and IAM boundaries.

Practice tracing DNS, HTTPS, load balancer frontends, serverless backends, and health evidence for public GCP services.

Practice separating Cloud Run ingress, IAM, egress, Direct VPC egress, private ranges, all-traffic paths, and startup failure evidence.

Practice choosing private access patterns for managed services while keeping DNS, network path, and IAM evidence separate.

Practice matching GCP runtimes to workload shape, start signal, scaling behavior, and operating responsibility.

Practice Cloud Run container contracts, services, revisions, traffic, scaling, runtime identity, configuration, and rollout evidence.

Practice VM-shaped responsibility: images, disks, zones, startup, process management, service accounts, networking, logs, and patching.

Practice event-shaped compute with events, triggers, handlers, invocations, retries, timeouts, identity, logs, and service boundaries.

Practice recognizing when Kubernetes is the requirement, then reviewing clusters, Autopilot versus Standard, workload objects, identity, and node responsibility.

Practice matching GCP data shapes to objects, relational records, documents, analytics tables, attached storage, and recovery copies.

Practice Cloud Storage buckets, objects, names, metadata, access, signed URLs, lifecycle, and versioning decisions.

Practice relational data shape, Cloud SQL instances, transactions, connections, private access, migrations, backups, and high availability.

Practice Firestore document modeling through documents, collections, paths, access patterns, indexes, transactions, and security boundaries.

Practice BigQuery analytics shape through datasets, tables, loading, queries, partitioning, clustering, cost habits, and data quality.

Practice attached-storage choices for GCP workloads that need block devices, snapshots, placement, or shared filesystem paths.

Practice recovery points, service-specific restore options, retention, safe deletion, and restore drills for GCP data.