Google Associate Cloud Engineer

For IT leaders: Moving workloads to Google Cloud without a trained team creates risk, including misconfigured IAM, overlapping VPC subnets, unmanaged billing, and Kubernetes clusters stood up before anyone understands them. This course gives your team a shared operating baseline for Google Cloud Platform (GCP), from creating projects and setting up Cloud Identity through daily operations: logging, monitoring and alerting, IAM, and service accounts.

Role fit: This course is a good fit for cloud admins and IT practitioners with about a year of cloud exposure. Learners should already know terms like hybrid, regions, and data warehouse, but they don't need deep implementation experience. Network professionals get a refresher on how GCP networking differs from on-prem. Learners who are new to Kubernetes are pointed to a separate fundamentals skill first.

Time investment: Plan for about 30 hours across 34 skills, most under an hour. Team Leads can assign skills in sequence or curate them into Playlists, such as GCP networking for infrastructure staff or compute, IAM, and operations topics for teams supporting cloud workloads.

Change-management notes: Each learner builds a GCP environment from scratch, so plan for sandbox projects and billing controls. The course covers both the Cloud Console and Cloud Shell or gcloud, so teams can standardize on CLI-driven, repeatable processes.

How CBT Nuggets supports the outcome: Validation exercises at the end of skills test scenario-based judgment. Examples include choosing StatefulSets versus Deployments, applying IAM roles appropriately, and building alert policies. This hands-on work supports Associate Cloud Engineer certification readiness, and Team Reporting lets Training Managers track progress across the team.

IT teams use this training to run Google Cloud with fewer surprises: patching fleets without taking services offline, choosing the right workload platform, and connecting cloud to existing networks reliably.

The course works through scenarios your team will face in production:

• Safer, staged patching: Using VM Manager, learners schedule OS updates and control how many VMs can be disrupted at once across zones, so maintenance doesn't take every instance serving a project offline at the same time.
• Right-sized workload decisions: Teams learn when a deployment with horizontal scaling fits a web app, when a StatefulSet fits a MySQL cluster that needs persistent storage, and when Cloud Run avoids the overhead of maintaining a Kubernetes cluster at all. That helps teams match each workload to a platform they can support.
• Reliable hybrid connectivity: Learners configure VPN tunnels between networks and design custom-mode VPC networks with non-overlapping subnets. These are the details that commonly stall cloud connectivity projects.
• Least-privilege access and operational troubleshooting: Practitioners manage IAM roles, users, policies, and service accounts while working through common cloud administration tasks. This gives employees the access they need without overexposing the environment.

Hands-on validation exercises after each skill, done in both Cloud Console and Cloud Shell, help learners build working command-line habits with gcloud and kubectl, not just exam recall.

Knowledge

• How Google Cloud organizes resources through projects, organizations, billing accounts, and Cloud Identity, and how organizational policies and roles apply across them
• When to choose Compute Engine, Google Kubernetes Engine, Cloud Run, or Cloud Functions, including GKE workload types like StatefulSets, Deployments, and standalone pods
• How Google Cloud networking differs from on-prem: VPC design (custom vs. auto mode), non-overlapping subnets, firewalls, ingress/egress, and high availability VPN with BGP
• Where event-driven patterns fit when connecting application components and routing activity between services
• How IAM roles and service accounts grant access with least privilege while still giving teams room to work
• How Cloud Logging and Cloud Monitoring surface problems through filtered logs, alert policies, and notifications

Ability

• Create and configure projects, enable APIs, manage users, and set up billing using both the Cloud Console and Cloud Shell
• Deploy autoscaled, load-balanced instance groups and manage operating system and application updates on Compute Engine VMs while limiting disruption
• Build and manage GKE clusters from the command line with gcloud and kubectl, and deploy containers to Cloud Run
• Configure custom VPC networks, firewall rules, and a high availability VPN between projects with matching BGP peers
• Deploy, load, and manage storage and database solutions and provision infrastructure as code
• Filter logs to isolate connection errors, create threshold-based alerts, and manage IAM policies and service accounts