Google Cloud Certifications: All in one Bundle

Learn how cloud identity and access management defines who can do what on which resources, with users, groups, roles, and service accounts, plus quotas and organization policy across the organization.

Learn how to create a free Google Cloud Platform account, claim $300 credit, set up the billing account, and monitor remaining credit as you explore paid services.

Explore the Google Cloud Platform console, navigate the dashboard, manage security and billing, and launch services from compute, App Engine, Cloud Functions, storage, and monitoring tools.

Explore Google Cloud's database and storage services, including Cloud SQL, Cloud Spanner, Cloud Bigtable for time-series workloads, Firestore, Cloud Storage, Memorystore, File Store, persistent disk, and AlloyDB for scalable data.

Explore Google Cloud Platform operations, including cloud logging and monitoring, dashboards and alerts, and tools like debugger, profiler, trace, and error reporting for app latency and reliability.

Explore google cloud platform network services, including vpc and subnet design across zones, firewall rules, routes, vpc peering, shared vpc, interconnect options, cloud vpn, and load balancers.

Get a high-level overview of cloud security, covering IAM permissions, data encryption at rest and in transit, confidential computing, Cloud KMS, Secrets Manager, web security scanner, and Cloud Armor.

Learn how to download, install, and initialize the Google Cloud SDK CLI, log in, and set a default project and compute zone (US West 1A), with gsutil and BigQuery utilities.

Explore Google Cloud Platform tools for managing applications, including Cloud Build for CI/CD, Cloud Scheduler for cron jobs, Cloud Task for workflows, Container Registry, Deployment Manager, and Identity Platform.

Gain a 10000-foot view of Google Cloud Platform by exploring core services across computing, networking, data and storage, and infrastructure management. Includes free-trial setup, regions and zones, and hands-on labs.

Explore Google Cloud compute services, including Compute Engine virtual machines, Container Engine for containers, App Engine as a platform, and Cloud Functions for event-driven workloads.

Explore Compute Engine in the Google Cloud Console, creating VM instances, managing disks, images, instance templates, and groups, while learning about networking, health checks, and marketplace options.

Create and configure a Google Cloud compute engine virtual machine from scratch or templates. Choose machine type, disks, networking, firewall, and security settings; learn startup scripts and encryption options.

Explore how to manage a compute engine instance: view status and networking, configure disks and SSH, monitor with logs, and adjust permissions on a running VM.

Explore the components and subsystems of Compute Engine, including console UI, disks and snapshots, container-optimized OS, IAM keys, labels, networks, and autoscaling with instance templates.

Understand Compute Engine storage options, including local SSD, zonal and regional persistent disks, cloud storage, and file storage. Learn to attach, resize, snapshot, and create boot and custom images.

Learn about container-optimized OS in Google Cloud, a chromium OS for container workloads with pre installed docker runtime, kernel locked down, automatic updates, and a locked-down default security posture.

Explore how labels identify Compute Engine resources and how tags enable firewall rules in Google Cloud VPC. Apply network tags to subnet firewalls and manage external and internal IP addresses.

Learn to use startup scripts in Google Cloud Compute Engine to install software, deploy applications, and run binaries at startup via inline, metadata, or cloud storage options.

Create and manage Google Cloud instance templates to define machine type, image, startup scripts, and metadata, then leverage managed instance groups for autoscaled, highly available VMs behind a load balancer.

Set quotas and limits to control cloud bills in public cloud environments by monitoring usage and restricting consumption, with easy access to view and adjust via the cloud console.

Explore Google Cloud spot instances, the preemptible VM, and learn how bidding, preemption signals, and 30-second shutdown scripts reduce costs by up to 90% while preserving data in persistent storage.

Explore sole tenancy in Google Cloud, reserving dedicated node hardware with node groups and node templates, configuring CPU, RAM, local SSDs, GPUs, auto scaling, and maintenance for secure, high-performance workloads.

Explore shielded VM with secure boot, Titan TPM, and integrity monitoring, and enable confidential computing on AMD EPYC processors to protect data in use in Google Cloud.

Explore Compute Engine as scalable virtual machines and contrast it with App Engine, GKE, and Cloud Function, highlighting infrastructure control, no-ops, and serverless options.

Explore Compute Engine machine types on Google Cloud Platform, comparing predefined standard, high memory, and shared core options with custom machine types, memory and vCPUs rules, and GPU restrictions.

Learn to configure Google Cloud Compute Engine, choosing general purpose or memory-optimized instances, from images and memory to disks, networking, and security options, including auto restart and delete protection.

Explore App Engine basics: deploy code or containers without provisioning infrastructure, enjoy auto scaling, traffic splitting, and a fully managed, open platform with standard and flexible environments.

Explore App Engine's hierarchy: applications, services, and versions, with traffic splitting and auto, manual, or basic scaling. Learn how instances run, set minimums, and view pricing and errors.

Learn how App Engine traffic splitting routes percentages to new app versions, gradually migrating traffic from v1 to v2 using IP-based, cookie-based, or random splits, and finalizing a complete switch.

Google App Engine standard environment, a predefined sandbox runtime with App Engine API support, secure isolation, and scalable instance classes with automatic, manual, or basic scaling.

Explore the App Engine deployment file app.yaml, covering standard and flexible environments, and configure service name, runtime, scaling, and resources like memory and disk size.

Learn to enable App Engine security scanner, configure firewall rules to allow or deny traffic, set up logging and debugging, and inspect source code for secure app deployment.

Explore App Engine pricing across standard and flexible environments, including instance class differences, resources billed (compute, memory, disk), cloud data store calls, search API, network traffic, and storage.

Learn how to prevent Google Cloud charges by disabling App Engine in settings, especially during the free trial, and manage default services by deleting extras so nothing runs.

Deploy your application on Google Cloud Platform using App Engine, offering standard and flexible environments, including docker containers, automatic scaling, and traffic splitting.

Discover how Google Cloud Kubernetes Engine manages containerized workloads with a master–worker architecture, API server, scheduler, and deployments, services, and ingress for scalable pod management.

Learn to practice Kubernetes locally with Minikube, create deployments and scale pods using kubectl, and compare with Google Cloud Platform's cloud features for certification readiness.

Learn to create a standard Kubernetes cluster on Google Cloud, compare autopilot vs standard, configure zones or regions, manage node pools with autoscaling, and understand blue-green upgrades.

Launch an autopilot cluster with default networking and container-managed workloads. Deploy nginx, expose it as a service, scale replicas, and rely on the cluster to manage pods, services, and autoscaling.

Explore Kubernetes pods, containers, and replica sets, with container-level resource requests and limits, and understand pod lifecycles and deployments. Also learn init containers and static pods that orchestrate pre-start tasks.

Learn how Kubernetes labels and selectors use key-value pairs to identify and select objects, apply node selectors, and perform equality-based and set-based filtering with kubectl.

Explore how Kubernetes uses namespaces to isolate objects within a cluster, apply resource quotas, and manage objects through YAML, deployments, jobs, and services.

Explore how Kubernetes manages the application lifecycle from design to production, highlighting pod priority and preemption, and deployment strategies such as rolling updates, canary, blue-green, and autoscaling.

Learn how to perform rolling updates in Kubernetes Engine, update deployment images, monitor rollout history, and rollback to a specific revision.

Understand ingress and ingress controller concepts for routing http and https from outside the cluster to internal services via routing rules and a load balancer, with nginx controller in GKE.

Explore Kubernetes networking, including container-to-container, pod-to-pod, and pod-to-service communication, plus external access, and learn how master and worker nodes, API server, and Flannel enable cross-node connectivity.

the kube scheduler filters feasible nodes for a pod by resource and port requirements, scores candidates with priorities like least requested and selector spread, and binds the best node.

Kubernetes engine monitors each node's health and replaces unresponsive nodes after about 10 minutes of health checks; enable node repair in standard engine settings to drain and recreate nodes.

Delete the cluster to remove all workloads and nodes, preventing ongoing charges for running nodes in your Google Cloud project.

Explore Kubernetes hybrid environments with Anthos, linking on-prem data centers to Google Cloud Platform, using service mesh, policy repository, and configuration management to manage clusters from a single console.

Kubernetes is an open-source container orchestration engine that runs on premises or public cloud, with Google Kubernetes Engine in Google Cloud Platform for cluster creation, deployments, services, and storage.

Explore cloud function basics, a serverless, event-driven compute service where you pay only for execution and can build APIs and microservices from event triggers.

Learn cloud run basics, see how it runs stateless containers in a fully managed, serverless environment, with auto scaling and per-request billing.

Discover how cloud run services operate as fully managed resources across regions or clusters, exposing unique endpoints and enabling domain mapping for your applications, with configurable service definitions and authorization.

Discover how Cloud Run revisions are immutable bundles containing environment variables, memory limits, and concurrency, and how deploying a new revision redirects traffic to the updated container.

Cloud Run spins up container instances based on concurrency; set a concurrency value (80 here) to trigger new instances, potentially up to 1000, with initial latency and cost considerations.

Explore Cloud Run fully managed features, including scale down to zero, up to 1000 containers, no provisioning, and pricing details like 2 million free requests per month.

Explore Cloud Run for Anthos, deploying containers as a service on clusters, choosing machine types, managing cluster capacity, and configuring custom domains with manual SSL.

Explore Cloud Run as a fully managed serverless platform for deploying containers, with configurable memory, timeout, region availability, and service accounts, plus access control for unauthorized invocations.

Explore software-defined load balancing in Google Cloud Platform, with global routing, health checks, and auto scaling directing traffic via intelligent routing to nearby healthy backend services.

Configure a load balancer in the cloud console by selecting network services, setting backend and frontend options, and attaching it to VMs or cloud storage, with future deep dives.

Explore how content aware load balancing routes video, images, and pages to dedicated backend services through front-end rules, with a default backend and load distribution algorithms that balance traffic.

Explore the load distribution algorithm for Google Cloud backends, configuring load distribution, balancing modes, capacity, and session affinity to distribute traffic across backend services.

Explore the various load balancers in Google Cloud Platform, including global external, regional external, and internal options, and learn how to choose the right type for single or multi-region deployments.

Configure a Google Cloud http load balancer by creating instance templates and groups, linking back-end services with Cloud DNS, and distributing traffic across Asia, Europe, and US West.

Explore global external load balancing with ssl proxy and dcp load balancers, leveraging intelligent routing and ssl offloading at the global layer 4 transport level to reduce backend load.

Learn how regional internal load balancers route traffic within a single region’s VPC, reducing latency. See how regional backend services and forwarding rules optimize internal traffic.

Demonstrate an optional http load balancer by creating regional instance templates and auto-scaling groups, configuring backend services and health checks, and routing traffic to the nearest region.

Demonstrates cleaning up a Google Cloud load balancer by deleting the load balancer, its instance template, the instance group, and all instances, then reviewing utilization.

Learn to configure Google Cloud load balancers, including global and regional options, SSL/TCP proxies, and UDP balancing, with backend routing, health checks, and security policies.

Explore Google Cloud data and storage services, including Cloud SQL, Cloud Spanner, Bigtable, BigQuery, Dataflow, and DataProc, with AI and natural language API.

Explore Cloud SQL, a fully managed Google Cloud database service for MySQL, PostgreSQL, and SQL Server, enabling OLTP workloads with high availability, backups, replication, and read replicas.

Explore Cloud SQL for PostgreSQL on Google Cloud Platform, a fully managed service with encryption at rest, SSL, multi-zone replication, and on-demand backups, import/export, and cloning.

Explore cloud sql performance parameters including cpu type, memory, network throughput, and storage type and size. Change machine types and storage configurations to affect throughput and IOPS.

Learn to manage Cloud SQL backups with automatic and on-demand options, set backup windows, initiate restores, and explore point-in-time restore and logging for reliability.

Explain how high availability in Cloud SQL uses synchronous replication between a master and a standby replica, enabling automatic failover across zones.

Discover how Cloud SQL read replicas offload read traffic to multiple replicas across zones, enabling read capacity despite vertical scaling limits, with the option to promote a replica to master.

Understand Cloud SQL pricing by selecting machine types and generations, with charges based on configuration and regional data egress. Delete unused instances to avoid ongoing costs.

Manage Google Cloud SQL as a scalable, hosted service by creating or migrating instances, configuring region, machine type, and storage; optimize performance and enable regional high-availability with seamless failover.

Explore alloydb, a PostgreSQL-like database from Google Cloud, offering horizontal scalability and fast oltp and olap workloads with a separate compute node and Colossus file system storage.

Discover how Cloud Spanner delivers a globally scalable, highly available relational database with strong consistency, multi-region replication, horizontal scaling, and interleaved tables for OLTP workloads.

Learn how to provision a Cloud Spanner instance, configure regional or multi-regional deployment with processing units and replicas, create databases and tables, and explore basic query performance and maintenance tasks.

Learn how Cloud Spanner performance scales with node count, regional versus multi-regional deployments, and how key distribution and hotspot avoidance drive query throughput and storage efficiency.

Explore Cloud Spanner’s interleaved tables by nesting albums and songs inside a single table, with up to seven levels of nesting and indexing on albums and related songs.

Explore Cloud Spanner identity and access management by mapping instance, database, and data permissions, including create, read, write, and session operations, and configuring instance settings.

Explore how Cloud Spanner delivers horizontally scalable, globally available data storage with regional configurations, data co-location, and pricing based on storage and nodes.

Learn google cloud storage basics with buckets and objects, scalable regional and multi-regional object storage for images, videos, and backups, featuring 11 nines durability and flexible access controls.

Create and configure cloud storage buckets, apply fine-grained or uniform access control, enable versioning and retention policies, and manage encryption and data protection in Google Cloud Storage.

Explore google cloud storage classes: standard, nearline, coldline, and archive, and learn to choose by access frequency, cost, and durability, while using lifecycle policies and auto class to optimize storage.

Explore cloud storage security concepts such as IAM policies, access control lists, signed urls and signed policy documents, and uniform bucket level access for project, bucket, and object permissions.

Learn how signed URLs grant time-limited access and permissions for objects, and how policy documents, conditions, and uniform public access prevent exposure while enforcing secure uploads and downloads.

Learn to configure object change notifications for a cloud storage bucket using cloud function triggers or pops up, covering event types such as finalized, metadata update, delete, and archive.

Explore how Google Cloud Storage uses default server-side encryption, optional customer-managed or client-side encryption with KMS or CSK, bucket locks for retention, and logging and monitoring for observability.

Explore Cloud Firestore in Datastore mode, featuring a strongly consistent storage layer, collection and document data model, and real-time updates for mobile and web clients.

Explore Cloud Firestore schema design by comparing relational models to Cloud Firestore collections and documents, mapping tables to collections, rows to documents, and fields to properties.

Explore cloud firestore pricing by comparing two mechanisms—document-based costs per 100,000 documents and storage costs per gigabyte per month—revealing that both databases share the same pricing structure.

Explore Cloud Firestore IAM concepts, permissions, and rules governing database operations such as insert, update, delete, get, export, and import, with roles like data store owner, app, reader, and writer.

Explore Cloud Firestore and Datastore as document storage on Google Cloud Platform, enabling Mongo-like storage with documents, queries, and index creation for efficient data retrieval.

Learn the fundamentals of Cloud Bigtable schema design, including single-index tables, row-level atomicity, and storing an entity in a single row with related data in adjacent rows.

Explore Cloud Bigtable performance factors by adjusting table nodes and disk types (SSD vs HDD), and how replication and load balancing affect throughput and query performance.

Understand how a Cloud Bigtable application profile defines how an instance handles incoming requests. Explore single-cluster routing vs multi-cluster routing and how single-row transactions and failover work.

Understand Cloud Bigtable quotas and limits, distinguishing soft limits from hard limits. Apply recommended sizes for column families, qualifiers, and cell values, and respect per-table and per-cluster constraints.

Explore how cloud Bigtable pricing varies with configuration, including node count, region, storage, and networking costs for development clusters.

Explore cloud iam for bigtable, outlining instance and cluster permissions, data-related admin permissions, and customizable access rules, including bigtable administrator, readers, and writers.

Explore big data solutions on google cloud platform, including cloud spanner, bigtable, datastore, cloud storage, cloud query, data flow, data lab, data catalogue, data fusion, data prep, and composer.

Explore BigQuery, Google's fully managed analytics data warehouse. Create datasets and tables, load data, and run fast per query analytics with automatic partitioning and no hardware provisioning.

Discover best practices for BigQuery query performance, data sources, and cost control, optimize bytes read, pass, and output, use partitioning, previews, storage strategy, and explore public datasets.

Learn how Cloud BigQuery uses IAM to control access with dataset roles (reader, writer, owner) and project-level permissions, enabling you to create, delete, and run jobs without managing hardware.

Explore Cloud BigQuery pricing with slot-based capacity, flat-rate options, and pre-run query estimation, and learn to use the bq command line to create datasets, tables, and run SQL.

Explore how Google Cloud BigQuery powers data warehousing and analytics by ingesting data from diverse sources, creating datasets, databases, and tables, and optimizing queries to control storage and query costs.

Launch and manage Hadoop and Spark clusters on Google Cloud Platform with Dataproc, using automatic cluster management, flexible machine types, persistent disks, and integrated storage for cost-efficient data processing.

Learn to enable the component gateway, adjust image and spark versions, and select optional components—Anaconda, Jupyter, Druid, Presto, and Zookeeper—to customize cloud dataproc clusters; these features install only when chosen.

Explore Cloud Dataproc storage options, including cloud storage buckets for backups, persistent disks for network storage, and local ssds, with implications that data cannot be recovered if the node fails.

Learn how to submit Google Cloud Dataproc jobs on a West 1 cluster using a jar and main class, run Spark and Presto jobs, and understand Dataproc versus Dataflow jobs.

Learn to design DAG-based Cloud Dataproc workflows that manage job dependencies, automate cluster lifecycles, and deploy template-driven pipelines using gcloud and parameterization.

Explore quotas and limits for Google Cloud Dataproc, including per-minute quotas: 400 total operation requests, 200 cluster operations, and 7,500 get job requests per minute, with free trial constraints.

Learn cloud dataproc pricing and how it scales with compute engine configurations, with pricing and CPI margins, plus using gcloud to manage dataproc domains: clusters, operation, workflow template, and jobs.

Delete the Cloud Dataproc cluster in Google Cloud Platform to save your 300 dollars, freeing all computing resources and completing the cleanup reminder before moving to the next service.

Learn how to launch and configure a Google Cloud Dataproc cluster with master and worker nodes, manage networks and staging on Cloud Storage, and design workflows for batch jobs.

Discover Cloud Dataflow, a managed Apache Beam service on Google Cloud for streaming and batch pipelines, enabling auto scaling and unified programming across fraud detection, analytics, and personalized user experience.

Compare cloud dataflow and cloud dataproc, showing dataflow's unified batch and streaming pipelines and dataproc's Hadoop and Spark clusters for existing ecosystems and notebooks.

Explore Google Cloud Dataflow quotas and limits, including per-minute requests, 1000 worker cap, project and organization concurrency, and templates for Pub/Sub, BigQuery, and Cloud Spanner to text files.

Assign job execution and control service accounts for Cloud Dataflow, granting access to cloud resources like Cloud Storage, Cloud Spanner, and Pub/Sub, and define roles for operators, developers, and administrators.

Explore cloud dataflow pricing, including per-second billing for batch and streaming jobs. Learn how memory, storage, and data processed costs depend on actual resource usage.

Explore how Google Cloud Dataflow enables building and running pipelines for batch and streaming data, from templates and Pub/Sub sources to datasets and Cloud Console jobs.

Discover how cloud pub/sub enables real-time streaming by connecting publishers, topics, and subscribers. It provides at-least-once delivery, exactly-once processing, and a managed open architecture with data plane and control plane.

Learn real-time messaging with cloud Pub/Sub by creating topics and subscriptions, publishing and pulling messages, and configuring acknowledgments, retention, and snapshots.

Explore data with Google Cloud Data Lab and Data Studio, connect to BigQuery, run notebooks in a VM, and use Python and machine learning for visualization and analysis.

Learn how Google Cloud Platform uses VPC networks, subnets, routing tables, firewall rules, and DNS to isolate resources, connect data centers, and manage networking services such as a load balancer.

Explore how VPC, subnets, and firewall rules secure isolated networks, and connect on premise data centers to cloud via interconnect and VPN, using DNS, load balancing, and caching.

Google Cloud VPC is a global, software-defined private network that spans regions and zones, enabling private resource communication, subnetting, firewall rules, routes, peering, and flow logs.

Explore the three vpc types on google cloud: default auto mode with prebuilt subnets and firewall rules, and custom vpc with user-defined subnets and ip ranges.

Manage projects as containers for cloud resources and use the default VPC, with up to five networks per project; for more networks, create another project and account for egress charges.

Understand Google Cloud VPC subnetworks, assign internal IP addresses to resources, and enforce firewall rules across production, development, and testing networks in regions and zones.

Allocate external IP addresses for Google Cloud VPC to enable VM access from outside the network, noting that unattached IPs incur charges.

Learn how cloud VPC firewall rules control ingress and egress traffic using IP ranges, protocols, ports, target tags, and priorities to allow or deny connections.

Learn how to set up a shared VPC with a host project and guest projects, manage network and security roles, and enforce permissions across an organization.

Explore vpc peering to connect networks inside or across projects, enabling vm-to-vm communication via pre-programmed routes while enforcing exclusive ip ranges and quotas for up to 25 direct peer networks.

Review Google Cloud VPC quotas and limits, including extendable quotas and a hard cap of 1000 instances per network. Learn about subnet limits, per-network constraints, and shared VPC attachment rules.

Understand cloud VPC flow logs that capture network traffic for monitoring, forensics, and real-time security analysis, and export logs to supported destinations.

Set up a bastion host to securely access internal servers, removing external IP exposure and connecting through an internal IP or DNS name during maintenance.

Learn how to use a bastion host to access a private vm by removing external ip, connecting via internal ip, and configuring virtual networking, interconnect, vpn, dns, and cloud seeding.

Enable host isolation by using a mapping gateway with IP forwarding to access an internal instance across networks, while applying firewall rules to restrict traffic and maintain security.

Learn vpc pricing basics: no charge to create a network, but traffic egress incurs costs; compare standard versus premium tier and their latency implications for regional traffic.

Explore cloud vpn and hybrid connectivity to securely extend your data center to Google Cloud, compare cloud interconnect and peering, and optimize egress costs.

Learn how cloud router enables dynamic routing between your data center and Google Cloud by exchanging routing information via BGP, replacing manual static routes.

Master cloud interconnect options to link your on-premises data center with Google Cloud Platform, including dedicated interconnect and partner interconnect, to access Google services and G Suite from on premises.

Learn how Cloud CDN uses Google's global fiber network and 80 PoP locations to deliver web content with low latency, SSL, and cache-driven efficiency.

Learn how Google Cloud DNS delivers scalable, high-availability domain name resolution via a global network, with programmable management of zones and record sets, low latency, and clear pricing.

Configure DNS to register a domain, set name servers, and map the domain to a load balancer and cloud storage bucket, illustrating DNS propagation.

Learn how Stackdriver applications provide multi-cloud monitoring, logging, and error reporting across GCP and AWS, with health checks, dashboards, log search, and latency tracing.

Learn to manage stackdriver applications by centralizing log entries from many sources, exporting to sinks, and choosing batch or near real-time delivery via Pub/Sub and cloud storage.

Learn Stackdriver Logging, a scalable multi-cloud log management service for storing, analyzing, searching, alerting, and exporting logs to Cloud Storage, Pub/Sub, or BigQuery.

Identify and monitor real-time application errors with Stackdriver error reporting, configure alerts, and analyze stack traces from the dashboard and mobile application.

Analyze end-to-end latency with Stackdriver trace to detect bottlenecks and performance issues. Collect near real-time latency data from App Engine and load balancer using the Google Cloud SDK.

Enable production debugging with Stackdriver Debug to inspect live code, add log points, and monitor logs and performance without redeploying.

Explore cloud identity and access management on the Google Cloud platform, from primitive access rules to fine-grained service accounts, and brush up on advanced concepts essential for security engineers.

Explore Cloud IAM basics, including identity and access management, primitive and fine-grained roles, organization policies, and audit trails that govern access across resources from organization to project.

Discover how Cloud IAM roles bundle permissions into primitive, predefined, and custom types, attach roles to users to grant access, and mix roles with permissions for fine-grained control.

Learn how Google Cloud IAM policies define resource-level access by binding roles to members, with IAM conditions enabling temporary access based on location and agent types.

Learn how service accounts enable secure app-to-app authentication in Google Cloud, enforce least privilege, and manage keys—Google managed vs user managed—and how to rotate and impersonate safely.

Explore why service account key rotation reduces leakage risk, compare push and pull models, and learn to use iam apis to create, upload, and manage short-lived credentials and impersonation.

Enable temporary access with short lived credentials and service account impersonation. They allow access without sharing keys to Google Cloud resources, using id tokens and JWTs.

Enforce organization-wide restrictions on service accounts and key creation, audit using IAM, delete unused keys, and apply least privilege with short-lived permissions to reduce impersonation risk.

Explore how cloud identity acts as a central identity platform for corporate, customer, and service identities, enabling centralized management and unified access across Google Workspace, Google Cloud, and third-party apps.

Learn how one-way gcds sync transfers on-prem ldap identities, including users, groups, and attributes, into cloud identity, with setup, testing, and best practices.

Understand how Google Cloud audit logs capture admin activity, data access, and system events. Learn how to enable, view, and export logs to BigQuery or Cloud Storage.

Learn how Cloud KMS enables encryption key management for Google Cloud services, including default encryption, customer supplied keys, and automated rotation, with IAM integration and Cloud Audit Logging.

Use the cloud security scanner to automatically scan App Engine and web apps for vulnerabilities. It detects flaws like flash injection, mixed content, and insecure JavaScript libraries.

Explore the Google Cloud source code repository on Google Cloud Platform to host unlimited private repositories, push and clone code, deploy apps, and debug with code search and GitHub integration.

Discover how the Google cloud container registry securely stores docker images, enables private hosting, automatic build and deploy, and image scanning for apps deployed on app engine.

Explore cloud build configuration files to define steps, docker builds, and deployment to cloud run, including environment variables, entry points, artifacts, secrets, and optional parameters.

Connect a repository, create a cloud build trigger, and deploy automated continuous integration pipelines that build docker containers and deploy to cloud run as changes push to branches or tags.

Explore a simple cloud build demo that builds a container from a Dockerfile, pushes to container registry, and runs on Cloud Run, with troubleshooting and region setup.

Demonstrate setting up a cloud build pipeline that uses a source code repository to trigger builds, create a container, push to the container registry, and deploy to Cloud Run.

Configure cloud build cd demo by creating a repository trigger, building a docker image, pushing to the container registry, and deploying to Cloud Run.

Explore cloud tasks in Google Cloud: leverage App Engine to distribute work via task queues, enabling synchronous, scalable, fully managed task execution with guaranteed delivery and a dashboard and CLI.

Create and manage cron-style tasks in Google Cloud using Cloud Scheduler, a fully managed service with Unix cron format, supporting App Engine and HTTP endpoints, time zones, and detailed logging.

Use deployment manager to automate cloud resource provisioning on Google Cloud Platform with infrastructure-as-code templates, configuration files, and VM instances.

Explore Google Cloud API management, featuring Apigee and Cloud Endpoints. Learn how to expose internal department APIs to external applications with discovery, contracts, security, monetization, and analytics.

Explore Apigee API management by creating proxy APIs, deploying to test and production, and applying shared flows and policies for spike arrest, quota, validation, and caching.

Explore Google Cloud developer tools and Eclipse integration, install the Google Cloud plugin via Eclipse Marketplace, and configure App Engine components to run and deploy apps locally with gcloud.

Configure IntelliJ with the Google App Engine plugin, set up the App Engine and Cloud SDKs, and create a new project using the guest book and opinion skeleton archetypes.

Explore the associate cloud engineer certification syllabus, from setting up the cloud environment to practicing services via labs, demos, and questions to build confidence.

Explore the associate cloud engineer syllabus and exam structure, a two-hour exam with 50 questions, covering cloud setup, deployment, IAM and security, billing, and compute resources like App Engine.

Develop exam-focused strategies for Google Cloud certifications by provisioning and managing cloud resources, monitoring production apps, and practicing sample questions to pass the exam.

Review the cloud certification syllabus and recap covered topics: setup cloud environment, plan and configure, deploy and implement, and ensure operation with secure access.

Set up a cloud solution environment by creating a project and account, managing users and APIs, provisioning strike driver workspaces, and configuring billing, budgets, alerts, and the command line interface.

Set up the Google Cloud environment by creating and managing projects, linking billing and accounts, enabling APIs and services, and configuring quotas within an organization hierarchy.

Configure budgets and alerts for billing accounts with thresholds at 50%, 90%, and 100%, then create service accounts with IAM roles to enforce quotas via console, CLI, and API libraries.

Explore planning and configuring a cloud solution on Google Cloud Platform by examining compute resources, data storage options, and network resources, using pricing calculator insights for budgeting and security.

Plan and estimate costs for GCP products using the pricing calculator, exploring discounts and sustained-use options for compute engine instances.

Explore the Google Cloud pricing calculator to estimate costs for Compute Engine, preemptible instances, storage, Cloud SQL, including discounts, free tiers, and sustained use.

Learn to estimate costs with the Google Cloud pricing calculator, exploring Compute Engine configurations, Cloud SQL, sustained use discounts, free tiers, and options like preemptible instances.

Learn to plan and configure cloud solutions using the pricing calculator, compute, storage, and network resources, while comparing services like Cloud SQL and Hadoop and exploring security and access controls.

View and manage audit logs for a project and its managed services, including admin activity, system events, and data access logs, with Stackdriver logging, per-service defaults, and BigQuery exports.

Master the professional cloud developer certification by learning how to deploy and develop applications, leverage cloud integrations and ready-made services, and manage services across cloud platforms for Google cloud certification.

Discover the professional cloud developer certification details, including the exam blueprint and registration fee, and master designing, deploying, integrating, and monitoring scalable cloud apps on Google Cloud Platform.

Develop exam strategy by mastering Google Cloud Platform services and deploying apps on App Engine and Compute Engine. Utilize Cloud Build, Cloud Run, and pricing insight for integration and performance.

Explore a local community app case study, its global expansion challenges, and how to design scalable, compliant cloud solutions on Google Cloud Platform with API management and analytics.

Explore the professional cloud developer syllabus and its mapping to certifications, then learn to design scalable cloud data applications and build, test, deploy, and integrate apps on Google Cloud Platform.

Explore how to design highly scalable, available, and reliable cloud-native apps, covering API design, secure deployments, data storage options, and multi-zone orchestration in cloud and datacenter contexts.

Compare infrastructure as a service, container as a service, and platform as a service, illustrating how cloud providers manage hardware, runtimes, and auto scaling to deploy and scale applications.

Explore portability and platform-specific design across development, test, and production environments, and learn how containers and configuration strategies reduce dependencies while guiding service choices.

Explore how Google Cloud Platform uses regions and zones and a private fiber network to deploy apps near customers, reduce latency, and compare premium versus standard networks and egress costs.

Learn to design microservices with an API proxy, using cloud endpoint to secure backend resources, enable logging, analytics, and monetization.

Design performant microservice APIs by filtering response attributes and using GraphQL to reduce payload, minimizing API management load and improving network efficiency.

Designing performant apps with database keys such as primary keys and index keys, using Cloud Spanner and Cloud Bigtable concepts on Google Cloud Platform.

Design session persistence by maintaining a user's login state across sessions with persistent storage. Use data store, Bigtable, Cloud Datastore, or memory cache to balance persistence and performance.

Explore how loosely coupled apps leverage Cloud Pub/Sub as a managed messaging gateway. It scales automatically, handling publishers and subscribers and routing messages to compute and analytics services like BigQuery.

Configure health checks on the load balancer to verify backend instances' health, using port 80 tcp probes every 30 seconds and an unhealthy threshold to stop routing to unhealthy instances.

Learn Google’s best practices for enterprise app design, covering project setup, identity and access management, networking, VPC, and firewall rules, plus logs and audit trails.

Set up your development environment with Google Cloud libraries, then build and test applications using a continuous integration pipeline, performance testing, agile coding, and monitoring and logging.

Configure budgets and alerts for your billing account to monitor costs and thresholds. Manage service accounts and permissions with identity and access management to control access to resources.

Deploy applications across compute engine, app engine, and cloud functions, and provision data and networking resources with Deployment Manager while managing service accounts, reinforced by demos and theory.

Explore integrating Google Cloud Platform services for data and storage, including persistent storage, databases, and file storage, and connect apps to compute services and cloud APIs.

Learn to integrate a Node.js application with Google Cloud SQL MySQL using a proxy, environment variables, and App Engine deployment, including creating a database and querying visits.

Learn how service discovery enables scalable microservices on cloud platforms, using API gateway, DNS proxy, and Spring Cloud patterns to register, locate, and route to multiple service instances.

Learn how to read and set instance metadata in Google Compute Engine to configure applications, including standard and custom metadata, via console and API, for identifying and categorizing instances.

Explore the foundation of cloud solution architecture for Google Cloud Platform, covering requirements, design, security and compliance, and optimizing hybrid cloud operations with data mart and multicore games case studies.

Learn the professional cloud architect syllabus and exam structure for Google Cloud, including planning and designing cloud solutions, security and compliance, resource management, and case-study driven questions.

Prepare for the Google Cloud certifications by mastering exam questions, core services, and case studies. Apply design and architecture thinking to compute, database, networking, big data, and machine learning services.

Prepare before the exam by working through case studies, developing solutions, and practicing solution architecture, since case studies will make up about 40 percent of questions.

Learn a case-study driven approach to cover the Google Cloud syllabus. Apply syllabus principles to real scenarios and architect and refine solutions with Google's guidelines for the architect exam.

Explore the TerramEarth case study of mining and farming equipment to design a scalable data pipeline that reduces unplanned downtime by enabling real-time and batch analytics on connected vehicle data.

Design cloud solution architecture by defining business and technical requirements, outlining network, storage, and compute resource ids, and creating a migration plan while envisioning future solutions.

Analyze and optimize technical and business processes using the public cloud platform’s ready-made features, including continuous deployment, troubleshooting, post-mortem analysis, service catalog, provisioning, and disaster recovery.

Analyze and define business processes for cloud adoption, communicate benefits to stakeholders, and align change management, skills readiness, and on-demand provisioning across multi-region resources to optimize costs.

Develop resilience in production by designing for failure, using multiple pub/sub topics and documented recovery procedures, and testing disaster recovery for regional outages.

Enhance reliability by implementing monitoring, logging, profiling, and alerting with deployment and release management for a hybrid cloud environment, and document audit-ready controls for the operations team.

Mountkirk Games case study shows deploying a cloud-based multiplayer backend on Google Cloud Platform for streaming analytics and reporting. It emphasizes auto-scaling, low-latency load balancing, and managed databases for insights.

Analyze the as-is architecture of Mountkirk Games, mapping the game backend, load balancer, and data flow into a MySQL database, then design a to-be architecture.

Design and plan a scalable, cost-optimized cloud solution architecture that meets business and technical requirements, enabling self-managed resources, external system integration, analytics, and KPI-driven metrics.

design a cloud-based game backend with 98% uptime and low latency by deploying resources near users, choosing virtual machines, NoSQL databases, and analytics pipelines using BigQuery or Spark.

Design resilient and scalable game backends on Google Cloud by selecting appropriate compute engine machines, auto-scaling, and load balancing. Implement security, disaster recovery, and monitoring to ensure reliable performance.

Analyze and optimize technical and business processes by migrating to Google Cloud Platform, leveraging real-time data from mobile devices, cloud analytics, and scalable architecture to improve performance and resiliency.

Learn to manage implementation for monaco came application development on Google Cloud Platform by applying best practices, testing frameworks, data and system integration, and Compute Engine migration tools.

Explore migrating Dress4Win to the cloud and mapping on-prem components to GCP services. Plan a disaster recovery site, assess architecture options, and adopt automation for scalable production.

Analyze EHR healthcare use cases to map platform and infrastructure components, including Kubernetes clusters, MySQL, Redis, MongoDB, and API/file interfaces, with a focus on disaster recovery, CI/CD, and regulatory compliance.

Map on-premise VMs and databases to Google Cloud using Compute Engine, Kubernetes Engine, and Cloud SQL or Firestore; enable scalable, observable, multi-region deployment with Anthos and CI/CD.

Explore migrating an existing asset to Google Cloud, enabling AI-driven race predictions, real-time telemetry, and global streaming with CDN, video intelligence, and Vertex AI.

Learn how to implement cloud DevOps practices on Google Cloud, building CI/CD pipelines, and mastering observability, logging, and troubleshooting best practices across cloud services and APIs.

Bootstraps a Google Cloud organization for DevOps, designs the resource hierarchy and infrastructure as code, and builds cloud-native or hybrid CI/CD architecture while managing dev, stage, production environments and permissions.

Design the organizational resource hierarchy with folders and projects, covering departments and billing. Review networking, vpc peering, identity and access management, permissions, and service accounts and keys for exam recap.

Explore resource management in Google Cloud, shaping policy by defining organization policies across organization, folders, and projects to enforce constraints, access controls, and trusted images for secure governance.

Learn how VPC peering connects two networks for VM-to-VM communication, including cross-project setups, quotas, nontransitive behavior, exclusive IP ranges, and its contrast with shared VPC.

Google Cloud IAM provides fine-grained access to Google Cloud resources across organizations, folders, and projects, with an audit trail and roles for identities such as accounts, groups, and service accounts.

Explore how iam policies provide access control for google cloud resources through policy objects and bindings, assigning roles to members and supporting parent to child inheritance and conditions.

Explore how service accounts authenticate service-to-service communication in Google Cloud, preserving least privilege by separating identities from passwords, with Google managed and user managed options and key rotation.

Discover how to use short lived service account credentials and impersonation to run gcloud commands and generate ID and JWT tokens with OAuth 2.0 and OpenID Connect, while auditing.

Explore Google Cloud networking basics, including global VPC networks, subnets, regions and zones, firewall rules, and peering options, with insights on load balancing and autoscaling.

Examine load balancers and their security perimeters, from global external http/https and tcp proxy to regional and internal options. Learn how anycast IP, failover, and cloud armor protect backend services.

Explore Google Cloud database and storage services while mastering data security practices, including encryption at rest and in transit, redaction, and DLP for protecting sensitive data.

Explore infrastructure as code in Google Cloud, including Cloud Foundation Toolkit, Config Connector, Terraform, Helm, and Deployment Manager, and learn best practices for provisioning, config management, and immutable architecture.

Design a CI/CD architecture stack in Google Cloud for hybrid multi-cloud environments, covering CI/CD concepts and tools like Cloud Build, Cloud Deploy, GitHub, Artifact Registry, and basic security.

Define the number and purpose of environments, ensuring dev, test, production isolation across projects and networks. Use Kubernetes Engine and Terraform with ci/cd tools like Git, Cloud Build, Spinnaker.

Learn how Google Cloud Source Repositories, a fully managed git service, provides unlimited private repositories, seamless integration with GitHub, fast code search, and quick deploy and debug.

Store and deploy Docker images in Google Cloud container registry with private secure storage, native Docker support, automatic builds, and vulnerability scanning for fast, highly available access.

Use Google Cloud Deployment Manager to automate infrastructure as code by defining resources in a configuration file and launching deployments from templates to provision compute resources, networks, and storage.

Master cloud build as a ci/cd tool on Google Cloud, turning code from Cloud Source or GitHub into Docker containers or Java archives via triggered build steps.

Connect a repository to cloud build, set triggers on branches or tags, and build with a dockerfile or yaml to deploy. Pricing: 20 minutes free, then 0.3 cents per minute.

Detail cloud build configuration with steps, environment variables, secrets, and artifacts in cloudbuild.yaml or cloudbuild.json, then build, push, and deploy Docker images to Cloud Run.

Show end-to-end cloud devops with Cloud Build, pushing code from a source repository, building a container, pushing to the container registry, and deploying to Cloud Run via a ci/cd pipeline.

Configure a cloud build workflow that builds a docker image, pushes it to the container registry, and launches a cloud run instance for a node.js hello world app.

Explore cloud task, a fully managed, scalable service for synchronous task execution on App Engine, offering Http targets, rate controls, retry policies, de-duplication, guaranteed delivery, and the command line interface.

Explore Google Cloud Scheduler, a fully managed cron-like service that delivers to App Engine, Cloud Pub/Sub, or HTTP endpoints, with configurable retry policies, Stackdriver logging, and Unix cron format.

Design and manage a CI/CD pipeline with artifact registry, deploy to hybrid and multi-cloud environments using Anthos and GKE, and explore deployment strategies, approvals, and binary authorization.

Implement ci/cd pipelines with auditing and tracking of dependent deployments. Emphasize deployment and rollback strategies using artifact registry, cloud build, cloud code, cloud deploy, and audit logs.

Explore deployment strategies—blue-green, recreate, canary with traffic splitting, AB testing, and rolling updates—driven by resource constraints, business goals, and safe rollbacks in Kubernetes.

Learn three approaches to securing a cloud ci cd deployment pipeline: vulnerability analysis with artifact registry, artifact scanning reports, binary authorization, and environment-specific iam policies.

This lecture explains securing infrastructure and applications by identifying common vulnerabilities, from injections to outdated libraries, and using web security scanner for design-time checks and cloud armor for production protection.

Learn to use Google's web security scanner, a security command center tool that detects web vulnerabilities on App Engine, Kubernetes Engine, and Compute Engine, with managed and custom scans.

Cloud armor protects production web apps by enforcing edge security policies at the POP, allowing or denying traffic by IP range or region, and blocking layer 3–7 threats.

Explore applying site reliability engineering practices to services, grounded in Google's SRE theory and the site reliability workbook, with practical guides for operating and implementing SRE across cloud environments.

Learn how site reliability engineering balances change, velocity, and service reliability, focusing on SLAs, error budgets, toil, automation, and incident management, with ITIL and Six Sigma context in DevOps.

Discover how DevOps and SRE merge to break silos, standardize tooling, and govern incidents with blameless post-mortems, SLOs, and error budgets for reliable, fast software delivery.

Learn how to define service level indicators (SLIs), set SLOs and error budgets, and balance releases with reliability signals like latency, availability, and durability across complex services.

Set up alerting policies that trigger on aggregated failures within fixed or moving windows, using error budgets and SLAs to avoid flooding, and translate alerts into incidents for timely response.

Define toil as manual, repetitive, value-free work that scales with a service, and explore automating provisioning, cleanup, and repetitive tasks to reduce toil and improve engineering efficiency.

Explore the service lifecycle from onboarding to retirement. Plan capacity with quotas and limits, enable autoscaling across GKE, Cloud Run, and Cloud Function, and use feedback to improve.

Explore healthy communications and blameless culture in operations, prevent burnout via toil reduction and automation, and manage incidents with post-mortems, root-cause analysis, traffic draining, and prioritized actions.

Implement service monitoring strategies by mastering cloud logging and monitoring tools, configuring the logging agent, and collecting structured and unstructured logs from Compute Engine, GKE, and serverless platforms.

Explore cloud logging in Google Cloud: configure, view, and analyze logs; export via sinks to Cloud Storage, BigQuery, or Pub/Sub; monitor with dashboards and alerts.

Explore firewall rule logs and VPC flow logs to audit access and monitor traffic between VMs and subnets. Learn how pocket monitoring mirrors traffic for forensic analysis.

Master cloud monitoring with alerts, uptime checks, and custom metrics that ingest logs and expose insights across hybrid deployments. Use incident reporting, autoscaling, and real-time error tracking with Stackdriver.

Explore logging and monitoring agents, including the ops agent, collectd, and fluentbit, and learn how to configure receivers, pipelines, and multi-line java logs for Google Cloud Platform services.

Explore Log Explorer, create dashboards and metrics, route logs to cloud storage or Pub/Sub, and analyze with BigQuery for alerts and monitoring.

Explore cloud monitoring in the console, setting dashboards, alerts, and incidents for VM and app metrics. Define services and SLOs, and monitor CPU utilization.

Explore managing metrics with cloud monitoring, dashboards and alerts, Terraform-defined alerting policies, and cloud managed Prometheus, plus enabling data access logs, VPC flow logs, and logging and monitoring access control.

Learn to filter and redact sensitive data in logs, use cloud data loss prevention to scan log buckets, and manage permissions to protect audit and VPC flow logs across projects.

Optimize service performance and cost optimization by using Cloud Operation Suite tools—monitoring, logging, trace, and profiler—while applying resource strategies like preemptible VMs and discounts.