[NEW] GCP Associate Cloud Engineer Certification Training

Master day-to-day user and group management in the Google admin console by resetting passwords, suspending and deleting accounts with data transfer, and adding members while promoting managers.

Verify your new project is linked to the correct billing account in the Google Cloud billing console, using Account Management to confirm or change the linkage.

Set up budgets and alerts to proactively manage cloud costs, noting budgets do not stop services; use scope, amount, and thresholds, and trigger programmatic actions via pub/sub and Cloud Run.

Set up a BigQuery billing export to capture detailed usage cost data, enabling SQL-based, resource-level analysis of cloud spending with forward-looking data that arrives after setup.

Master IAM fundamentals to secure your cloud by applying least privilege, mastering principles, policies, and the golden rule, configuring basic, predefined, and custom roles, and managing service accounts and impersonation.

Identify who can do what on which resource in Google Cloud by binding principals users, groups, or service accounts to roles through allow policies and role bindings, with inheritance.

Create a custom IAM role to grant only the permissions to start and stop existing compute engine instances, assign it to a junior developer, and verify least privilege access.

Manage service accounts at scale by creating with purpose, clear naming, and least-privilege through impersonation. Monitor with IAM recommender and audit logs, and retire accounts using a disable-then-delete lifecycle.

Learn identity and access management in Google Cloud, focusing on principals, roles, and policy, least privilege, and predefined or custom roles, with secure service accounts and impersonation.

Explore Google Cloud Compute Engine by provisioning Linux and Windows VMs, securing access with SSH and RDP, and mastering storage, snapshots, and disaster recovery via VM Manager.

Launch a Windows Server 2022 data center with Desktop Experience on Google Cloud, note licensing costs and a 50 gigabyte boot disk, plus RDP access on port 3389.

Launch a spot VM in Google Cloud Compute Engine, set provisioning model to spot with stop termination, and test preemption by stopping and restarting to confirm persistence on persistent disk.

Attach a new persistent disk to a running Linux virtual machine in US Central 1C, format and mount it, then resize the file system to use the added space.

Protect cloud virtual machines by creating a boot disk snapshot, performing a point-in-time backup, simulating a disaster, and restoring the VM to recover data.

Automate backups with snapshot schedules that define frequency and start time, enforce retention, and automatically delete old snapshots, while attaching schedules to regional persistent disks.

Compare containers and virtual machines to show speed, efficiency, and isolation. Explain when to use GKE or Compute Engine for cloud-native apps and legacy workloads.

Explore GKE Autopilot's zero node management and pod-based billing versus standard's full control over node pools, machine types, GPUs, and scaling.

Deploy a GKE autopilot cluster in the cloud console, enabling the API, configuring DNS-based access, and connecting with kubectl to manage a serverless, highly available, managed cluster.

Deploy a regional GKE standard cluster with configurable node pools for high availability. Learn to set node counts per zone and choose upgrade strategies and provisioning options.

Explore how to configure private and public control plane endpoints for private GKE clusters, secure with authorized networks, and understand Cloud NAT and private Google access for safe networking.

Learn to use kubectl as the universal command line for Kubernetes, configure access with kubeconfig via gcloud get-credentials, and troubleshoot with kubectl describe to manage GKE clusters.

Create a Flask web app with app.py and requirements.txt, build a Dockerfile using Python 3.11, build a container image with docker build, run the container, and prepare for Artifact Registry.

Learn how Artifact Registry replaces the older deprecated container registry, the gcr.io service, and stores Docker images and other artifacts in regional, multi-format repositories with IAM-based push and pull permissions.

Deploy your first container to a GKE cluster with kubectl create deployment using the full artifact registry image path, and learn how deployments manage pods.

Learn to observe and troubleshoot your GKE cluster by inspecting nodes, pods, and services with kubectl, using get and describe commands, and reviewing events and allocatable resources.

Explore how GKE node pools organize a cluster into departments with identical configurations, enabling cost control, hardware distinctions, and auto-scaling for diverse workloads.

Explore how the horizontal pod autoscaler scales out by adding pods to meet cpu utilization targets, and preview the vertical pod autoscaler to balance resources across deployments.

Configure the horizontal pod autoscaler using CPU metrics, set resource requests, verify with kubectl top pods, and enable the cluster autoscaler for elastic node scaling in GKE.

Learn to securely access GCP services from GKE pods using workload identity federation, binding a Kubernetes service account to a Google service account for seamless token exchange.

Cloud Run auto-scaling creates container instances automatically and scales to zero when idle, with configurable minimum and maximum instances to balance latency and cost.

Explore real-world Cloud Run functions use cases, including cloud storage triggered thumbnails, pub slash sub welcome emails, and real-time security alerts via cloud logging, all in an event-driven, decoupled pattern.

Configure a cloud run function named process-image that triggers on cloud storage uploads via event arc, using a custom service account and python 3.12, with inline code and dependencies.

Compare VMs, GKE, and Cloud Run services and functions along a spectrum of management to decide the right compute option for each workload.

Compare compute options across VMs, GKE, and Cloud Run, highlighting when to choose Cloud Run services for stateless http apps scalable to zero, and Cloud Run functions for event-driven code.

Explore object storage with a shelf analogy, defining buckets, objects, and metadata in Google Cloud Storage, and explain flat and hierarchical namespaces, durability, availability, and unstructured data uses.

Create a Google Cloud bucket in a chosen region with standard storage, uniform bucket level access, and public access prevention; upload files and folders with soft delete seven days.

Plan and execute large-scale migrations to Google Cloud Storage with Storage Transfer Service, a fully managed pipeline for cloud-to-cloud and on-premises transfers, offering incremental transfers and end-to-end checksum validation.

Discover how Google Cloud Storage stores data as objects in buckets with 11 nines of durability, while you manage access, lifecycle, versioning, and cost for unstructured data.

Cloud SQL provides a fully managed relational database service for MySQL, PostgreSQL, and SQL Server, with automatic backups, patches, vertical scaling, and storage auto-scaling.

Bigtable is Google's fully managed, wide-column NoSQL database built for petabyte-scale analytics and ultra-fast throughput. It separates compute from storage for instant scaling and avoids hotspotting with smart row-key design.

Centralize monitoring for all Cloud SQL, Spanner, Firestore, Bigtable, AlloyDB, and Memorystore databases with Database Center's unified dashboard. Get health status, performance metrics, and cost insights across your portfolio.

Identify key cost drivers across Cloud SQL, AlloyDB, Spanner, Firestore, Bigtable, and MemoryStore, and apply right-sizing, single-zone deployments, read replicas, and caching to optimize costs.

For a global, relational ledger with strong external consistency and high write throughput, choose Cloud Spanner for SQL compatibility, global multi-region replication, and horizontal scaling.