Define cloud computing by comparing it to data centers, emphasizing renting and pay-as-you-go models, elastic resources, and access to AI services like NLP, vision, and ML.

Discover Google Cloud computing services, including Compute Engine virtual machines, App Engine and Cloud Run for platform as a service, Kubernetes Engine for clusters, and Cloud Functions for event-driven code.

Explore Google Cloud object storage and database services, including buckets, Cloud SQL, Cloud Spanner, Cloud Big Table, Cloud Fire Store, and BigQuery for diverse data needs.

Explore Google Cloud networking with VPC subnets, regional resources, firewall rules, IP addresses, and serverless VPC access enabling App Engine, Cloud Functions, and Cloud Run to access internal networks.

Explore Google Cloud's observability with Cloud Monitoring and Cloud Logging, big data tools like Dataproc, Dataflow, and BigQuery, and Vertex AI for AI and machine learning, plus Deployment Manager.

Navigate the Google Cloud Console portal interface, manage projects, search and access cloud services like Cloud SQL and BigQuery, and use Cloud Shell and notifications for efficient cloud management.

Discover how to use Google Cloud Console's Cloud Shell to access a Linux virtual machine, run commands, and persist data with script storage and tab completion.

Open the Cloud Shell Editor in the cloud console to edit files, create new ones, edit json files, save example.txt, and run Linux commands such as ls in your browser.

Explore Cloud SDK commands with gcloud for compute and storage, from Cloud Shell or local install, plus gsutil for cloud storage and brief BigQuery and Bigtable commands.

Create and manage Google Cloud billing accounts by linking projects to billing accounts, selecting an organization, adding payment methods, and ensuring you have the owner or project billing manager role.

Enable the required cloud service APIs from the console, browse the API library, and turn on tools such as cloud data fusion to access services while controlling costs.

Explore the Google Cloud resource hierarchy, from the organization to folders and projects, and apply inherited access controls and policies to manage ownership and resources.

Learn to assign a billing account to a project in the console, navigate to account management, and use the menu to disable or change billing.

Explore how identities such as Google accounts, service accounts, groups, and Google Workspace are granted predefined, custom, and basic roles, enforced by policies and bindings across the resource hierarchy.

Explore identity and role management in google cloud by viewing principals and service accounts, and viewing by roles, comparing editor, dlp administrator, and owner.

Explore how iam roles are organized by service, compare App Engine viewer and admin permissions, and learn permission naming patterns used by BigQuery and other services.

Create a custom role in the IAM console, assign App Engine Deployer permissions to get and list instances, and manage its lifecycle with the principle of least privilege.

Navigate the IAM console to assign and remove roles, granting the Cloud Run developer role and saving changes to manage permissions.

Prefer predefined roles and use custom roles only when needed to maintain least privilege; use groups for roles, manage service accounts individually, and enable auditing with log export.

Create a new virtual machine in Google Cloud using the Cloud Console, choosing from templates, a machine image, or the marketplace, and configure region, zone, machine type, storage, and optional GPUs.

Learn to create a virtual machine with cloud shell using gcloud compute instances create, specifying name, zone, and machine type, then review boot disk options and generated code.

Install the Cloud SDK locally, run the installer, and accept components such as emulators and bq; then initialize with gcloud init, authenticate, and set the ACE project region and zone.

Create a virtual machine with the Google Cloud SDK using gcloud compute instances create, specifying ace-instance in us-west1-b with machine type t2d-standard-1 and boot-disk-size 200 gig.

Access the compute engine console, connect to a running Ubuntu VM via ssh, and run Linux commands like pwd, nano, ls, cat, and top. For Windows, use rdp.

Explore the compute engine console to view virtual machine inventory, filter by name, zone, and labels, and use and/or logic to narrow large lists, then examine different machine engine types.

Explore Google Cloud machine configurations and types, including general purpose, compute optimized, memory optimized, and GPUs, with series and platforms like C2, C2D, H3, N1, N2D, and E2.

Discover how to attach an additional disk to a Google Cloud virtual machine, choose disk type and size, enable encryption and labels, and manage disks via console or gcloud.

Configure a GPU-enabled virtual machine with two NVIDIA v100 GPUs and a CUDA-ready or deep learning image, and monitor costs to shut down when not in use.

Learn to define a custom machine type by selecting custom in the machine configuration and setting two vcpus and six gigabytes of memory.

Learn to create a disk snapshot for backup using the compute engine console, naming and describing the snapshot, selecting the source disk, applying regional storage, and adding environment labels.

Discover how spot instances lower costs by 60–90 percent and enable fault-tolerant workloads with Kubernetes Engine, Dataproc, and managed instance groups.

Explore Google Cloud's managed and unmanaged instance groups. Managed groups use an instance template and offer auto scaling, auto healing, multi-zone deployments, and auto updating.

Create a managed instance group in Google Cloud Compute Engine, configure Ubuntu-based instance template, enable auto scaling to 70% CPU with min 1 and max 4, and review health checks.

Understand autoscaling in managed instance groups, which adds or removes instances based on workload with minimum and maximum numbers and utilization using cpu, http load balancing capacity, or Stackdriver metrics.

Explore Google Kubernetes Engine as a managed platform for deploying containerized applications on clusters of nodes, orchestrating containers with load balancing, node pools, autoscale, autohealing, and cloud monitoring and logging.

Explore the high level Kubernetes architecture, including the control plane, pods, deployments, services, and storage concepts, and learn how GKE cluster components coordinate resilient, scalable workloads.

Create a Google Cloud Autopilot cluster in the console, compare Autopilot with standard mode, and configure cluster basics, fleet, networking, and advanced settings for the setup.

Create a standard mode Kubernetes cluster in Google Cloud, compare it to autopilot, and manage node pools, autoscaler, networking, security, and upgrade strategies.

Deploy a new Kubernetes cluster using Cloud Shell and gcloud container clusters create, naming ace exam cluster two in us-west1-b with n1-standard-1 and 100 gigabytes, and prepare for deploying applications.

Deploy an application as a workload in a Kubernetes Engine cluster using an existing Nginx container, review its YAML deployment, and verify the deployment in the workloads list.

discover Cube Control, a command line utility for running commands on a Kubernetes cluster, and see how it complements the gcloud container commands used to manage clusters.

Discover GKE utilization, observability, and cost optimization by reviewing CPU, memory, and disk usage and leveraging cloud monitoring metrics and billing reports to optimize workloads.

Explore Kubernetes logs for observability using cloud logging and Logs Explorer to filter, drill down, and correlate log messages across a cluster, including SQL and Postgres services.