Google Certified Professional Cloud Architect 2025

Explore the Google Cloud professional cloud architect certification, covering design and provisioning of secure, compliant cloud solutions, case studies, and exam topics on core services, IAM, monitoring, and reliability.

Explore cloud migration strategies through two case studies, TerramEarth and Dress4win, highlighting IoT data collection, data analysis, predictive maintenance, and cost-aware cloud adoption.

Explore a high-level overview of Google Cloud Platform, covering free trial setup, account login, regions and zones, projects, billing, quotas, pricing, and core roles and permissions.

Log in to Google Cloud Platform to access a $300 free credit valid for 12 months, set up a billing account, and explore services in the console.

Explore regions and zones as independent geographic areas hosting data centers worldwide to reduce latency, improve availability, and secure data at rest and in transit with renewable-energy powered facilities.

Explore Google's global network backbone, including fiber optic links, submarine cable investments, and the point of presence that connects data centers and CDN locations, with premium charges for cross-region data.

Explore Google Cloud Platform services, from compute, storage, networking, and databases to identity, security, big data and machine learning; discover no-ops platforms, management tools, and API management with Apigee.

Explore the three interfaces to Google Cloud Platform: the web console, command line tools like gcloud, and API client libraries, plus how to enable APIs and use cloud shell.

Explore how Google Cloud Platform resources—global, regional, and zonal—live inside a project, with folders and departments guiding structure, billing, and policy-driven access.

Explore identity and access management in Google Cloud Platform, learn to create and assign granular permissions and rules to users, groups, or service accounts across organization, folders, and projects.

Learn how to create and manage a GCP billing account, link projects to a single billing account, set budgets and alerts, change payment methods, and generate billing reports.

Learn how quotas control resource usage in Google Cloud, including global, project, and region quotas, and how to request increases or upgrade accounts, with hard limits like VPC peering.

Understand how a service account enables application-to-application access to cloud storage, with permissions and optional keys, and how its three types (manual, virtual machine, and API internal) govern read access.

Explore Google cloud infrastructure services for resource management, access controls, monitoring with Stackdriver, logging, tracing, and deployment management using infrastructure as code to deploy repeatable configurations.

Create a Google Cloud Platform account, access the cloud console, and explore regions, zones, data centers, and POP locations; learn about projects, billing, IAM, service accounts, and the pricing calculator.

Access Cloud Shell, a browser-based, secure, fully configured command-line interface with gcloud, BigQuery, and Cloud Storage to deploy, test, and manage Google Cloud apps from anywhere.

Explore the GCP mobile app that interfaces with the backend to monitor, alert, and manage cloud resources on the go, including logs, billing alerts, and starting or stopping instances.

Install the Google Cloud SDK on your laptop to access gcloud, Cloud Shell, and built-in client libraries for storage and APIs.

Explore cloud APIs by using the cloud console and API Explorer to enable, query, and test compute engine APIs, view instance details, and learn how to call a programming interface.

Explore Google Cloud Platform development tools, including Cloud SDK, Cloud Shell, Cloud Storage, and bq for queries, plus Cloud Source Repositories and plugins for Android and Firebase deployments.

Explore Google compute services from compute engine virtual machines to container engine, App Engine, and Cloud Functions, covering infrastructure, platform, and serverless deployment options.

Google Compute Engine offers a flexible virtual machine service on Google Cloud, enabling on-demand sizing, autoscaling, GPUs, and secure, customizable CPU, memory, and storage options.

Take a guided tour of the Google Cloud Compute Engine console, covering VM instances, instance templates, disks, images, instance groups, and marketplace options.

Launch a Google compute engine vm from scratch or template, configure machine type, boot disk, networking, security, and startup scripts, then set availability and spot options for cost savings.

Explore Compute Engine basics with a live Linux VM demo, covering instance details, networking, disks, SSH access, monitoring, and management tasks like start, stop, delete, and permissions.

Explore Google compute engine machine types, from general purpose to custom configurations, across N1, N2, and C3 generations with per CPU memory. Understand CPU architectures and GPU requirements, including drivers.

Explore compute engine disks, snapshots, and images to optimize storage choices. Compare local ssd, zonal and regional persistent disks, file storage in nfs, ram disk, and custom and public images.

Discover container optimized OS for compute engine. Run containers out of the box with pre-installed docker and automatic weekly updates, while offering a locked-down, small attack surface.

Learn how instance templates define machine type, image, startup script, disks, and metadata for use by managed instance groups, enabling autoscaling, autohealing, rolling updates, and high availability with load balancers.

Explore how quotas and limits control cloud usage in Google Cloud, AWS, and other public clouds, enabling monitoring, fairness, and cost management through quota requests and adjustments.

Explore how Google Cloud spot instances reduce costs by 60–90% for fault-tolerant workloads. Use bidding, preemption signals, and a 30-second notice to save progress to persistent storage.

Learn how sole tenancy reserves dedicated hardware using node groups and node templates, with CPUs, RAM, local ssd, and optional GPUs for gaming, finance, healthcare, and ml workloads.

Explore Google Compute Engine pricing, including per-second disk charges, snapshot costs, geography-based pricing, and up to 80% discounts on preemptible VMs, sustained use, and committed use.

Protect Google Cloud workloads with shielded VM and confidential computing by securing boot with titan-chip TPM and integrity monitoring, while enabling in-memory protection on AMD EPYC processors.

Learn to create and configure Linux virtual machines in Google Compute Engine, selecting regions and zones, choosing machine types, disk images, and SSH access with cost-conscious options.

Learn to create a Windows virtual machine in Google Compute Engine, select a Windows image and suitable size for cost and performance, and connect using a password.

Understand Google Cloud load balancers: external global and regional HTTP/HTTPS, TCP, and UDP types with proxy or pass-through modes, anycast IPs, and Cloud Armor protection.

Explain how a content aware load balancer routes front-end requests to video, BHP pages, or image content from a cloud storage bucket via routing rules to a default backend.

Demonstrates configuring a Google Cloud HTTP load balancer with regional instance templates and groups, health checks, and auto scaling to route traffic across Asia, Europe, and US West.

Learn how to clean up a Google Cloud HTTP load balancer by deleting the load balancer, then the instance group and its associated instances.

Explore how Google App Engine organizes apps into services, each with multiple versions and instances, and learn traffic splitting, scaling options, and basic monitoring and pricing.

Explore App Engine traffic splitting to deploy a new version, gradually migrate traffic from old to new using IP, cookie, or random splits, and complete the cutover.

Explore App Engine's flexible environment, running your own containers and bringing your own language. Compare it to the standard environment, noting VM access, weekly patches, and region-based deployment.

Explain the App Engine standard environment as a sandbox with predefined runtimes and App Engine API, enabling scalable, secure isolation and deployment from Cloud SDK. Manage via Google Cloud Console.

Learn how the app.yaml deployment file configures App Engine standard and flexible environments, covering service name, scaling, memory, disk size, and instance limits.

Understand how App Engine pricing differs between standard and flexible environments, including instance class effects. Learn charges for cloud datastore calls, search API, network traffic, blob storage, and logs API.

Enable the App Engine security scanner, configure firewall rules to allow or deny traffic, run scans, and use logs, tracing, and reporting to debug and examine the source code.

Discover how Google Cloud Kubernetes Engine manages containerized microservices with master and worker nodes, API server, scheduler, controllers, pods, deployments, services, namespaces, and autoscaling.

Learn how to practice Kubernetes engine locally with Minikube, deploying nginx via kubectl, scaling replicas, and inspecting pods, while noting differences from cloud deployments on GCP.

Launch a standard Kubernetes cluster, choose zone or region and a Kubernetes version, then configure node pools, autoscaler, and blue-green upgrade strategies while deploying and exposing an nginx workload.

Explore pods, deployments, and replica sets in Kubernetes engine, learn how containers define resource requests and limits, and use init containers for prechecks and startup housekeeping.

Learn how Kubernetes uses labels as key-value pairs to identify and group objects, and apply equality and set-based selectors, such as environment=production and accelerator=Nvidia Tesla P100, to filter resources.

Explore the Kubernetes engine lifecycle by performing rolling updates on a deployment, updating container images, and practicing rollback using kubectl rollout history and undo to specific revisions.

Explain how ingress and ingress controller expose http and https routes from outside the cluster, route traffic to multiple services with routing rules, and use NGINX controller in GKE.

Explore Kubernetes networking, including container-to-container, pod-to-pod across nodes using flannel, and pod-to-service with CIDR-based addressing, plus ingress and load balancer for external access.

Explain how the kube scheduler filters and scores nodes to place pods by resources, labels, affinity, and taints, and how to run multiple schedulers or create a custom one.

Enable Google Cloud logging and monitoring for Kubernetes Engine; deploy node logging agents to collect container stdout and stderr and route to Cloud Monitoring with cluster, node, pod, container filters.

Explore how Anthos unifies on-prem and cloud environments, enabling multi-cluster management, service mesh, configuration and policy governance, and seamless connectivity via cloud interconnect and APIs.

This cloud function demo guides you through creating a function, uploading or sourcing code from cloud storage, configuring triggers, and testing with input data while adjusting memory and timeout settings.

Cloud run runs stateless containers in a fully managed, serverless environment, enabling secure, scalable production workloads with billable container time; choose fully managed or cloud run on a gk cluster.

Understand Cloud Run services: fully managed, replicated across region zones, and exposing a unique endpoint; create multiple services mapped to your domain without tight VPC coupling and define service authorization.

Understand how cloud run revisions are immutable bundles that carry environment variables, memory limits, and concurrency, and how deploying a new revision redirects traffic to the updated container.

Describe how Cloud Run uses container instances and a concurrency value to scale traffic, including spin-up behavior, latency, and the associated cost tradeoffs.

Discover cloud run fully managed mode, scaling to zero or up to 1000 containers with no provisioning, plus pricing and free-tier limits.

Cloud Run for Anthos lets you deploy a container as a service on Kubernetes, whether on premises or inside VMs, with configurable machine types and memory constraints.

Explore how Google container engine orchestrates clusters, pods, and deployments. Compare App Engine and Cloud Function use cases for scalable, event-driven workloads.

Navigate Google Cloud data and storage services, from Cloud SQL and Spanner to Bigtable and Datastore, and from BigQuery to Dataflow and Dataproc.

Discover Cloud SQL basics for MySQL, PostgreSQL, and SQL Server on Google Cloud, with managed instances, backups, encryption, and availability. Explore performance factors like storage type, IOPS, and read replicas.

Explore Cloud SQL for MySQL, a managed MySQL instance on Google Cloud Platform with encryption at rest and in transit, SSL, replication across zones, import/export, and backups with point-in-time recovery.

Discover Cloud SQL for PostgreSQL on Google Cloud, a fully managed PostgreSQL service with encryption, SSL, multi-zone replication, backups, import/export, and cloning; note that point-in-time recovery is not supported.

Explore how cloud performance depends on cpu, memory, network throughput, and storage type (ssd or hdd), and how changing machine types and automatic storage scaling affect throughput and io.

Learn how Cloud SQL backups work with automatic and on-demand backups, create and restore backups using seed backups, adjust backup windows, and explore point-in-time recovery and logging for reliability.

Learn how Cloud SQL logging enables point-in-time recovery by using binary logging and redo logs to restore a database to its exact state after a crash.

Learn high availability in Cloud SQL through synchronous replication to a primary, create failover and read replicas, and enable automatic switchovers across zones in the same region.

explore read replicas in cloud sql to offload read queries from the primary. create replicas across zones, understand lag in replication, and promote a replica to master when needed.

Learn Cloud SQL pricing by selecting machine types and configurations, understanding generation-based charges and egress fees within or across regions. Labs guide you to delete instances to avoid charges.

Explore a Google Cloud MySQL demo: launch a second-generation instance, tune IOPS and throughput, enable backups and point-in-time recovery, and create and connect to master and read replicas.

Explore a hands-on PostgreSQL demo in Google Cloud, creating and managing a cloud instance with machine type, IOPS, maintenance windows, passwords, and basic network settings.

Learn to clean up Cloud SQL resources by deleting instances after use to avoid charges and to complete ongoing operations.

Explore Alloydb, a Google Cloud PostgreSQL–like database designed for horizontal scaling and high-speed OLTP and OLAP, with compute and storage separation and fourfold transaction speed and hundredfold analytics boost.

Create and configure a cloud storage bucket with multi regional or dual region locations, standard storage class, fine grained access control, and enable versioning, retention, encryption, and observability.

Explore standard, nearline, coldline, and archive storage classes, their use cases and pricing, and learn how lifecycle rules and auto class optimize data storage.

Explore cloud storage security concepts such as IAM and IAM conditions, ACLs, signed URLs and policies, uniform bucket level access, and project versus bucket level permissions, with object-level controls.

Explore signed URLs, policy documents, and condition-based access to cloud storage; implement uniform bucket access, disable public access, and enforce IAM conditions for time-limited permissions.

Move data from on-premises or other clouds to Google Cloud using the storage transfer service, cloud-to-cloud transfers, and optional transfer appliances; manage jobs via the Google Cloud Console or API.

Describe server side and client side encryption in Google Cloud storage, with Google managed keys or KMS/CSK. Explain bucket lock, retention policy, and admin and data access logs for monitoring.

Explore Google persistent disk, a network storage for virtual machines that supports dedicated or read-only shared disks, online scaling, snapshots, encryption, and cost-effective performance options.

Explore Google Cloud Spanner, a globally scalable, strongly consistent relational database with SQL support, schema, horizontal scaling, multi-region availability, splits, interleaved tables, and point-in-time recovery.

Provision a Cloud Spanner instance, configure regional or multi-regional settings, adjust processing units and replicas, and create databases and tables using Google standard SQL or PostgreSQL.

Learn how Cloud Spanner performance scales with node count, regional vs multi-regional deployments, and how queries per second and storage vary, while distributing keys to avoid hotspots near your consumer.

Explore Cloud Firestore in Datastore mode, a backward-compatible, strongly consistent storage layer that extends Datastore with real-time updates, offline features, and increased scalability for mobile and web apps.

Compare two pricing mechanisms for Cloud Firestore: document storage and data storage, with rates such as 18 cents per 100,000 documents and 18 cents per GB per month.

Explore Cloud Firestore IAM concepts, including database and file storage permissions, common operations (insert, update, delete, get, export, import), and rules like data store owner, app, reader, and writer.

Explore Cloud BigQuery basics. It is a fully managed analytics data warehouse on Google Cloud Platform; create datasets and tables, load data from upload, and run scalable per-query analytics.

Learn BigQuery best practices for query performance and cost control, including bytes read, bytes passed to each stage, and partitioning for targeted data.

Understand cloud IAM in BigQuery by managing dataset permissions and job creation with dataset reader, writer, and owner roles, without provisioning any hardware.

Master the basics of Google Cloud Dataproc by launching Spark and Hadoop clusters, configuring master and worker nodes, and integrating with other cloud services for scalable, cost-efficient analytics.

Migrate on-premises jobs to Google Cloud Dataproc by creating and deleting clusters for Spark or Hadoop workloads, with outputs to BigQuery or Cloud Storage, and schedule using Apache Airflow.

Learn to customize cloud dataproc clusters by enabling or disabling features, selecting a gateway, choosing an image version, and adding optional components like Zookeeper, Presto, Druid, and Jupyter.

Learn cloud dataproc storage options, including persistent disk, local ssd, and cloud storage backups, and how data durability and backups affect node failures and data shuffling.

Explore how to submit a Dataproc job on a chosen cluster, run Spark jobs with a main jar, and understand the difference between Dataproc and Dataflow jobs.

Explore Cloud Dataproc workflows to design and execute directed acyclic graphs with dependencies, using templates, managed or existing clusters, and parameterized, gcloud-driven automation.

Explore how Dataproc IAM governs clusters, jobs, operations, and workflow templates, with rules that assign permissions like create, get, list, update, and delete to users.

Explore Cloud Dataproc pricing and how it maps to compute engine configurations, with standard pricing varying by usage, then learn cluster, operations, workflow templates, and jobs.

Delete the Dataproc cluster on Google Cloud Platform to save your $300, releasing all computing resources. Select the cluster, choose delete, and confirm to complete the cleanup.

Explore Cloud Bigtable basics: a low-latency, high-throughput NoSQL columnar database with time-series support, autoscaling, seamless HBase migration, and automatic data rebalancing for real-time analytics and IoT.

Understand cloud bigtable architecture: clients connect to front-end pools; nodes store metadata while data sits in the Colossus file system as SSTables, with automatic node balancing.

Migrate from HBase to Bigtable to gain scalable, fully managed storage on Google Cloud, resize clusters without downtime, and access data via application configuration or HBase-compatible clients.

Learn the basics of schema design for columnar storage rooted in the Bigtable foundation, with one index per table, single-row entities, and adjacent rows for related data.

Explore how Bigtable performance depends on processing nodes, storage filesystem, disk type (SSD or HDD), replication, and load balancing, and how increasing nodes boosts query throughput.

Explore soft and hard quotas for Google Cloud Bigtable, including recommended limits on rows, column families, qualifiers, and cell sizes, plus maximum tables per cluster.

Understand how Cloud Bigtable pricing characteristics change with configuration choices, including node count (single vs multi-node development clusters), region, storage such as SSD or HDD, and ingress/egress fees.

Explore Cloud IAM for Bigtable, detailing instance and cluster permissions, admin-related permissions, and roles such as Bigtable administrator, readers, and writers, plus custom rules.

Explore Cloud Dataflow, the Apache Beam implementation on Google Cloud Platform, a managed service to deploy and run pipelines for stream and batch processing to data tables and BigQuery.

Compare Cloud Dataflow and Cloud Dataproc for batch and streaming pipelines; use Dataproc with Hadoop and Spark ecosystems and notebooks, and Dataflow for unified, portable pipelines.

Learn the Google Cloud Platform Dataflow quotas and limits, including compute engine instances, data upload, monitoring requests, and shuffle slots, and know when to use Dataflow versus data proc.

Discover how Google Cloud Dataflow pricing works, with per-second billing based on actual usage of batch and streaming jobs, memory, storage, and data processed, and how compute resources drive charges.

Create and publish to a Cloud Pub/Sub topic, then create a subscription to pull messages with acknowledgment and retention settings; explore snapshots and cloud storage notifications.

Explore data with cloud data lab, an interactive notebook-based tool for exploration, analysis, and machine learning that integrates with BigQuery, data flow, and visualizations.

Explore Google Cloud networking products, including global VPC with subnets, load balancing, Cloud DNS, VPN and Cloud Interconnect, and Cloud CDN, with emphasis on logs, security, and low-latency connectivity.

Explore Google Cloud Platform's virtual private cloud (VPC) as a global, software-defined network with subnets, firewall rules, routes, flow logs, and shared VPC, vpn, and cloud router connectivity.

Explore the three VPC types, including default auto mode VPCs with precreated subnets and firewall rules, and custom VPCs with user-defined subnets, IP ranges, and routing.

Explore how projects host VPC networks, with a default VPC and up to five networks per project. Learn to plan for global networks, inter-network traffic, and internet egress charges.

Design VPC subnets to segment production, development, and testing networks with firewall rules and access controls; subnets provide internal IP addresses and can span zones or regions.

Explore how VPC internal IP addresses come from subnet ranges, with static IPs tied to a VM and ephemeral IPs that change, and how internal DNS resolves instance names.

Discover how external IP addresses in Google Cloud expose VMs to the internet. Learn to reserve or release static IPs, and differentiate them from internal IPs while managing cost implications.

Explore how Google Cloud VPC routes direct traffic between subnets, to the internet, or to the data center via VPN, using default routes, next hops, instance tags, and firewall rules.

Explore how google cloud vpc firewall rules control inbound and outbound traffic with allow and deny actions, using ip ranges, protocols, ports, and tags.

Learn how a shared VPC uses a host project to provide a centralized network for guest projects, with centralized network and security administration, policies, and defined roles.

Set up shared VPC across projects by creating a G Suite organization, configuring host and service projects, and managing subnets and network admin permissions for secure cross-project access.

Learn how VPC peering connects multiple networks across or within projects, with exclusive IP ranges, non-transitive routing, quotas on peering and VMs, and the shared VPC model for centralized management.

Explore VPC quotas and limits, distinguishing extendable quotas from non extendable ones, and note caps such as up to 1,000 instances per network, no subnet limit, and shared VPC constraints.

Explore vpc flow logs to understand how traffic flows, enabling network monitoring and real-time security analysis, and optimize expenses by exporting logs to a supported destination or cloud storage.

VPC pricing bills only egress traffic, not network creation, with standard and premium tiers. Premium routes traffic via Google fiber for lower latency, while standard uses public internet.

Learn to configure a bastion host in GCP's virtual private cloud, remove external IPs, and access a VM via ssh from outside, plus interconnect, vpn, cloud dns, and cloud shell.

Explore Google Cloud's virtual private cloud nat gateway, installing a bastion host for maintenance, and route traffic by ip forwarding between internal addresses, while firewall rules isolate traffic.

Recap core GCP VPC concepts: networks and subnets, global networks and region-specific subnets, default and custom networks, firewall rules, routes, and internal and external IP addresses with egress traffic.

Learn hybrid connectivity between your data center and Google Cloud Platform using cloud interconnect, cloud VPN, and peering, with direct, partner, and internet options for secure, low-latency data exchange.

Explore cloud router demos, configure VPN gateways and tunnels, and test dynamic routing with BGP to connect subnets across Europe west and Asia east.

Explore Google Cloud DNS, a highly available, low-latency managed domain name service that translates domain names to IPs, supports programmable records, zones, and cost-effective pricing.

Explore cloud interconnect and vpn options for connecting on-premises to Google Cloud, including dedicated interconnect, partner interconnect, and IPsec VPN with Cloud Router, plus DNS and CDN caching and pricing.

Explore cloud identity and access management on Google Cloud Platform, from primitive access rules to fine-grained service accounts, highlighting advanced concepts and features essential for security engineers.

Explore cloud IAM basics, defining identity and access management, primitive and fine-grained roles, organization policies, and audit trails to control enterprise-scale access across resources from organization to project.

Assign roles to users to grant permissions, using primitive, predefined, and custom roles; combine permissions to create tailored roles, with examples like editor, owner, and compute engine instance admin.

Learn how service accounts enable secure app-to-app authentication in Google Cloud, enforce least privilege, manage keys, and choose between Google managed and user managed service accounts, including impersonation and rotation.

Learn why service account key rotation reduces risk when keys are compromised and compare push and pull rotation methods, including required permissions to create and upload new keys.

Learn to use short lived credentials and service account impersonation to access cloud resources securely, and generate oauth2 id tokens and jwt tokens while rotating keys.

Explore workload identity federation for on-prem access to Google Cloud services by creating a pool, authenticating with an identity provider, obtaining a token, and impersonating the user to access resources.

Enforce organization-wide constraints on service accounts at all levels. Audit accounts with IAM, delete unused keys, and limit key creation; use short-lived permissions and policy intelligence.

Explore how cloud identity centralizes corporate, customer, and service identities, enabling single identity management and role-based access to Google Cloud services and third-party apps.

Explore how GCDS enables a one-way sync from on-prem LDAP identities—organization units, groups, and users, along with emails, profiles, calendars, contacts, and passwords—into cloud identity.

Learn how resource hierarchy and organization policies enforce rules across organizations, folders, and projects, with top-down constraint propagation and examples like resource location, allowed APIs, and trusted images.

Explore how to secure Compute Engine instances by configuring IAM access scopes, default vs custom service accounts, and firewall controls, including bastion host, NAT, and SSH key management.

Secure the Kubernetes engine by hardening the control plane, master and worker nodes, and pod access with service accounts, secrets, and workload identity.

Explore confidential computing with shielded VM in Google Cloud, defending against rootkits and bootkits while protecting secrets with TPM and Titan, through secure boot, measured boot, and integrity policy baselines.

Explore confidential computing in Google Cloud Platform, enabling a trusted execution environment that processes data in memory with keys in the processor on AMD EPYC CPU, offering isolation and attestation.

Explore how VPC service controls create perimeters around resources, enforce ingress and egress rules, and leverage access context manager to define fine-grained, context-aware access levels and service parameters.

VPC service controls create perimeters around resources with vertical and horizontal boundaries, using context-aware parameters and access context manager to enforce ingress and egress to prevent data exfiltration.

Implement zero-trust security for BigQuery with IAM, VPC service controls, and authorized views. Enforce encryption, short-lived credentials, DLP, and deletion policies for datasets, tables, and retention.

Identify common vulnerabilities, including injections and DDoS, and secure infrastructure with design-time code scanning, a web security scanner, and Cloud Armor protection in production.

Discover how Google Cloud Security Command Center unifies vulnerability and threat reporting, health analytics, and scans in a single dashboard, with standard and premium tiers and broad integrations.

Explore regulatory compliance in cloud environments, including PCI DSS, ISO 9000, and government laws, and learn how cloud providers offer tools to achieve compliance through encryption and framework alignment.

Understand PCI DSS standards for payment card processing and the roles of manufacturers, developers, and merchants, and implement controls like firewalls, data encryption, access restrictions, and regular security testing.

Explore hipaa compliance for health care providers, plans, clearinghouses, and associates, and implement encryption, authentication, access logging, device protection, risk management, staff training, and breach notification processes.

Explore GDPR, CCPA, and other privacy laws, and learn how to ensure transparency, consent, data minimization, accuracy, deletion, encryption, two-factor authentication, and audit trails in Google Cloud.