Google Cloud Certifications: All in one Bundle

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.

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.

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.

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.

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 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 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.

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.

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 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.

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.

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.

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.

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.

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 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.

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

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 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.

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.

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 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.

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.

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

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 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.

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

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

Discover cost optimization strategies using preemptible spot instances with discounts, committed use discounts, and sustained use discounts, plus premium versus standard network tiers to balance latency and efficiency.

Explore Google Cloud DevOps principles through global and golden signals, SLOs and SLAs, blameless post-mortems. Learn incident command roles, lifecycle steps, root cause analysis, and practical monitoring and tracing tools.

Explore Google Cloud foundation concepts, including least privilege, organization policy, service accounts, and VPC security, with practical guidance on firewall rules, shared VPC, and private access.

Learn how regression predicts continuous outcomes with a linear model y = mx + c from inputs, and how classification assigns categories via logistic regression and probability thresholds.

Explore how accuracy differs from precision and recall, and learn to evaluate models with true/false positives and negatives, roc auc, and f1 for better model performance.

Learn how feature cross and one hot encoding create synthetic features that capture interactions between inputs. Use L1 regularization to prune unimportant features and encode categorical data for robust models.

Explore how neural networks use hidden layers, activation functions like ReLU, tanh, and sigmoid, and backpropagation to model input-output relationships for deep learning, including softmax for multi-class classification.

Embeddings use vector representations to capture similarity between users and items, guiding recommendations from watch history and movie features; neural networks with embedding layers boost accuracy.

Gain an overview of Keras within TensorFlow Lite, compare sequential and functional models, and trace the end-to-end machine learning workflow from data preparation to model deployment.

Explore building a simple TensorFlow Keras sequential model to predict y from x using a basic linear equation y=2x+1, train with data, tune learning rate, and visualize results with TensorBoard.

Learn how the knn classifier uses training data to predict labels with a lazy training approach, adjustable k and distance or uniform weighting, contrasted with k-means concepts.