GCP Professional Cloud Architect: Google Cloud Certification

Explore cloud fundamentals, learning why startups and enterprises move from data centers to the cloud, understand servers and data centers, and discover the advantages with a visual, example-driven approach.

Explore why enterprises need thousands of servers and how data centers, with web, file, email, and database servers, support scalable, reliable applications.

Discover what a data center is and its power, cooling, networking, and security needs. Learn the challenges of owning one—high upfront costs and capacity guessing—and how cloud enables instant servers.

See how cloud computing mirrors electricity by renting infrastructure instead of owning data centers. Use on-demand access, elasticity, and pay-as-you-go to rent servers as needed.

Explore how cloud benefits organizations by shifting to pay-for-use opEx, achieving economies of scale, stopping capacity guessing, boosting speed and agility, going global in minutes, and avoiding undifferentiated heavy lifting.

Master elasticity, agility, and geo-distribution to serve global users efficiently. Optimize latency and availability while trading CapEx for OpEx through pay-as-you-go and economies of scale.

Explore elasticity, OpEx pay-as-you-go, agility, and availability through scenarios like Netflix scaling and 24/7 healthcare, plus geo-distribution, low latency, and economies of scale.

Explore how Google Cloud uses a private fiber optic network to deliver faster, secure data; learn its heritage, mission, and catalog across compute, storage, databases, analytics, AI, and machine learning.

Create a Google Cloud account to access a $300 free credit trial by providing personal details and a valid card, then verify your mobile number to start the free trial.

Maximize learning efficiency by speed watching, using 1.25x–2x playback to save time while balancing comprehension, starting at a slower speed and incrementally increasing.

Explore regions and zones, learn why multiple data centers across regions boost latency and availability, and see how cloud providers offer global regions to keep applications running.

Choose cloud regions by balancing compliance, latency, service availability, and pricing to keep data local and close to users while ensuring needed services are available.

Deploy your applications across multiple regions to achieve low latency, expand your global footprint, and meet data residency regulations while improving high availability and disaster recovery.

Learn how zones, or availability zones, provide high availability within a single region by deploying across multiple isolated zones with independent power and networks connected by high-speed links.

Explore how regions and zones enable disaster recovery, low latency, and data residency. Learn multi-zone and multi-region deployment strategies, plus high availability concepts, common misconceptions, and practical scenarios.

Explore how regions and zones in Google Cloud are named and structured, with examples like us-west1 and us-west1-a. Learn how deploying across multiple zones or regions boosts high availability.

Deploy your first virtual machine on Google Cloud using Compute Engine, exploring machine types, images, and ip addresses. Learn startup scripts, instance templates, custom images, and basic troubleshooting.

Google Compute Engine enables provisioning and management of virtual machine instances, including start, stop, restart, and termination, as well as load balancing, auto scaling, attached storage, and network configuration.

Learn to create Google Compute Engine VM instances, manage their lifecycle via start, stop, and restart, and configure SSH access, labels, region, image, and HTTP firewall.

Choose the machine family and type to match your workload, then select an image—public or custom—to set the operating system and software on the VM.

Install apache2 on a Google Compute Engine virtual machine, update packages, and configure a custom index.html that displays hello world from the VM hostname and IP address.

Explain the difference between internal and external IP addresses on Google Cloud Platform VMs, noting external IPs are internet addressable and can change, while internal IPs remain fixed.

Learn to reserve a static external IP address in Google Cloud, assign it to a VM, and keep it after stop or restart, unlike ephemeral addresses.

Learn to manage static IP addresses in GCP by switching addresses between VM instances, keeping them attached when stopped, and releasing unused IPs to avoid charges.

Explore how to bootstrap a Compute Engine VM by using a startup script to automatically install Apache, configure the firewall, and deploy a sample page, improving security and efficiency.

Learn how to simplify vm creation with instance templates, including startup scripts and image family options, while noting templates cannot be updated and can launch single or grouped instances.

Reduce boot time by using a custom image with OS patches and software pre-installed; create, harden, and share images across projects, then deploy via instance templates.

Troubleshoot Apache on a GCP VM by verifying the external IP and URL, SSHing into the instance, checking /ware/www/html/index.html, and starting the service.

Navigate the Google Cloud Platform web console, pin favorites with the pencil, access Kubernetes Engine and dashboards, and review APIs, billing, monitoring, and project settings.

Discover the prerequisites for creating a virtual machine in Google Cloud, including a project, a billing account, and enabled Compute Engine APIs. Explore sole tenancy, VM Manager, and firewall basics.

Discover the origin of in28minutes and how daily learning, curiosity, and viewing change as opportunities fuel your cloud certification journey.

Explore instance groups, including managed and unmanaged types, and master cloud load balancing concepts to deploy multi-version microservices across regions with multiple managed instance groups.

Learn to manage Google Cloud instance groups, including managed instance groups with auto-scaling, auto-healing, and rolling updates, and unmanaged groups for diverse VM configurations across zones and canary redeployments.

Create a managed instance group using an instance template, configure autoscaling with cpu utilization, load balancer utilization, or stackdriver metrics, cool-down, scale-in controls, and http health checks for auto healing.

Manage a managed instance group with health checks to keep two healthy instances running across zones, and adjust auto-scaling thresholds to set min and max instance counts.

Update a managed instance group with rolling updates using a new template, optionally with canary testing, and proactive or opportunistic timing, adjusting max surge and max unavailable.

Distribute user traffic across VM instances in single or multiple regions using cloud load balancing, featuring health checks, auto scaling, single Anycast IP, and internal load balancing.

Understand how two systems communicate across network, transport, and application layers using IP, TCP, TLS, UDP, HTTP, and HTTPS, and how REST API calls flow securely across these layers.

Create and configure a load balancer to distribute traffic across a managed instance group, configuring the http front end and a backend service with host and path rules.

Explore cloud load balancing terminology, including backends (managed instance groups), front end, and host and path rules, and learn how SSL/TLS termination shapes traffic between clients, load balancer, and VMs.

Explore how a Google Cloud load balancer distributes traffic from the front end to a managed instance group via a backend service, with health checks and monitoring.

Select the correct Google Cloud load balancer by internal versus external traffic and HTTP, TCP, or UDP types, including new regional and global HTTPS options with advanced traffic management.

Compare the features and differences among cloud load balancers, including external http/https proxy load balancers, pass-through versus proxy behavior, ssl offloading, and internal versus external tcp/udp options.

Explore how HTTPS Cloud Load Balancing distributes traffic across regional managed instance groups in multiple regions, ensuring high availability and low latency by routing to healthy backends nearest to users.

Explore how https load balancing supports multi-regional microservices with multiple versions. Configure backend services, backends, and URL maps to route traffic using host and path rules and front end.

Learn to design available and scalable Compute Engine and load balancing solutions, then explore security, performance, and resilience, plus sustained use discounts, committed use discounts, and preemptible VMs.

Explore Compute Engine and Load Balancing for architects, and learn to design highly resilient, scalable, secure, and cost-efficient Google Cloud solutions that meet business and nonfunctional requirements.

Availability measures the percentage of time an application provides expected operations, with four nines (99.99%) and five nines (99.999%) benchmarks. Online apps target 99.99% availability, including frontend, APIs, and databases.

Implement high availability for compute engine and load balancing with regional instance groups across regions and global https load balancing, using health checks and live migration for resilience.

Explore scalability as the system’s ability to adapt to changing demand by increasing resources or instances. Compare vertical and horizontal scaling, noting load balancers, availability, and limits.

Explore vertical scaling by upgrading E2 machine types and stopping the vm, then implement horizontal scaling with regional or zonal instance groups, auto-scaling, and cloud load balancing.

Learn how live migration keeps VMs running during maintenance by migrating instances within the same zone, with availability policy controlling on host maintenance and automatic restart.

Learn to enable GPUs in Google Compute Engine to accelerate AI and graphics workloads, including GPU configuration options and using images with GPU libraries.

Apply firewall rules to restrict ingress and egress, and use internal IPs. Select the right machine family, consider sole-tenant nodes, and use GPUs or TPUs with hardened images.

Learn to design resilient cloud architectures with Cloud Load Balancing across regional instance groups, using Cloud Monitoring and Cloud Logging for proactive recovery via health checks and live migration.

Learn about sustained use discounts for Google compute engine and Kubernetes engine, automatically reducing VM costs after 25% monthly usage, with higher discounts for greater usage.

Explore committed use discounts for Google Cloud virtual machines, offering up to 70% off for one- or three-year commitments on Compute Engine and GKE, with discounts auto-applied to new instances.

Learn how preemptible VMs offer short-lived, low-cost compute for fault-tolerant, non-immediate batch workloads, with limits like 24-hour max, no SLA, and no automatic restart.

Explore Spot VMs, the latest preemptible VMs with no maximum runtime, reclaimed by Google Cloud with 30 seconds notice, offering 60–91% discounts under dynamic pricing.

Compute Engine charges accrue by the second after a one-minute minimum; stop a VM to pause billing, use budget alerts, and export billing to BigQuery for cost tracking and discounts.

Explore cost optimization for google cloud compute engine and load balancing by enabling auto scaling, leveraging sustained use and committed use discounts, and using preemptible VMs for noncritical, fault-tolerant workloads.

Learn how to use the gcloud command line interface to manage Google Cloud resources via the Cloud SDK or Cloud Shell, including initializing configurations and listing active settings.

Master the gcloud command structure by exploring groups, subgroups, and actions, and practice compute commands like instances list, create, describe, and delete with region and zone context.

Explore how Cloud Shell provisions a Google Compute Engine VM with 5 gb free persistent disc storage in the home directory, and enables SSH into VMs via private IP addresses.

Explore managed services in cloud computing, moving beyond manual configurations to iaas, paas, faas, and caas, and learn the serverless terminology that enables scalable deployment.

Compare IaaS and PaaS, and see who handles the operating system, runtime, and scaling. Learn examples like EC2, App Engine, Elastic Beanstalk, and container as a service or function as a service.

Explore microservices and containers, creating Docker images that run anywhere, and master container orchestration with features like auto scale, service discovery, load balancing, health checks, and zero-downtime deployments.

Learn serverless computing: you focus on code while cloud services manage infrastructure, scale automatically, and you pay per invocation, not for servers.

Explore a 10,000-foot overview of the different managed compute services offered by GCP, the Google Cloud Platform, including Compute Engine, Kubernetes Engine, App Engine, Cloud Functions, and Cloud Run.

discover how google app engine, a core managed service in gcp, enables easy deployment and automatic scaling with end-to-end management, multiple runtimes, container support, and traffic splitting.

Explore App Engine's two environments, standard and flexible, highlighting language-specific sandboxes, V1 and V2 differences, runtime restrictions, and Docker-based containers with Compute Engine VMs and background processes.

Learn how App Engine organizes apps into a single application per project, with multiple services and versions, each with instances, plus traffic splitting and rollback.

Compare App Engine standard and flexible environments, covering pricing, scaling options, startup times, scale-to-zero, storage, and debugging features, to decide the best fit for workloads.

Learn how automatic, basic, and manual scaling options manage App Engine instances, including when to use continuous workloads, ad hoc workloads, and predictable loads.

Deploy a Python 3.9 app to App Engine standard by creating a project, enabling the App Engine API, and deploying with gcloud; configure Cloud Build access via storage object viewer.

Explore the App Engine dashboard to manage services, versions, and instances, observe traffic splits, and learn command line workflows for listing and deploying versions (including v2) with gcloud.

Deploy V3 without promoting traffic, test it via a browser URL, then gradually split traffic between V2 and V3 by IP, random, or cookie, until all traffic favors V3.

Learn to create additional App Engine services and manage versions, migrate and split traffic, deploy with app.yaml, and access per-service URLs for hands-on cloud architecture practice.

Explore Google Kubernetes Engine (GKE) and Kubernetes as the leading container orchestration tool, learning cluster management, autoscaling, service discovery, load balancing, auto repair and upgrades, and integrated logging and monitoring.

Create a GKE cluster by enabling the Kubernetes Engine API, choose standard or autopilot, and use gcloud container clusters create to deploy a microservice.

Connect to the cluster from cloud shell, deploy a microservice with kubectl using a docker hub image, and expose it via a load balancer on port 8080.

Explore a GKE Kubernetes cluster in the GCP console, view master and worker nodes, manage node pools (including GPU options), inspect deployments, pods, logs, services, and Ingress routing.

Scale deployments with kubectl and observe load-balanced pods, then resize node pools with gcloud to prepare for autoscaling in Kubernetes.

Learn to implement autoscaling for microservices and Kubernetes clusters using horizontal pod autoscaler and gcloud updates, and manage configuration with config maps and secrets.

Learn to deploy Kubernetes resources with YAML declarative configuration, edit deployment replicas via the web console or kubectl apply -f deployment.yaml, and compare declarative with imperative approaches.

learn to deploy a gpu-enabled microservice on kubernetes by creating a gpu node pool and using a deployment yaml node selector with cloud.google.com/gknodepool.

Explore how Kubernetes clusters run workloads, with a master node as the control plane managing API server, scheduler, etcd, and how worker nodes run deployments via kubelet.

Identify pods as the smallest deployable unit in Kubernetes, hosting one or more containers with ephemeral IPs. Learn how deployments create pods, share networking and storage, and monitor pod statuses.

Deployments manage multiple versions of a microservice and enable rolling upgrades with zero downtime. ReplicaSets maintain a specific number of pods for a version and replace old sets during updates.

Expose your Kubernetes deployments with a service to shield external users from internal changes, using cluster ip, load balancer, or node port, and route with an ingress when needed.

Discover how ingress routes external HTTP traffic to multiple microservices using path-based rules, delivers SSL termination, and uses a single load balancer instead of separate load balancers per service.

Store your microservice images in Google Container Registry, a fully managed private repository in Google Cloud, push with Cloud Build, and secure and analyze images while enforcing policies.

Learn a high level view of creating docker images with a dockerfile, focusing on lightweight alpine images, proper layering, and separating dependency installation from code for faster builds.

Explore essential GKE scenarios for cost optimization with preemptible VMs and committed discounts, GPU-enabled node pools, autoscalers, sandboxing with GKE Sandbox, and internal cluster communication via cluster IP.

Learn to clean up a Kubernetes environment by deleting the service, deployment, and cluster with kubectl and gcloud, including project cleanup and best practices for teardown.

Review Kubernetes core hardware and software terms, including clusters, master and worker nodes, and node pools. Learn how pods, deployments, and services enable scalable, managed workloads.

Stay relevant by learning at work through questioning decisions and processes, surrounding yourself with curious colleagues, attending conferences, and tracking industry trends with resources like the Thoughtworks Radar.