
Explore Google Cloud certifications in a bundle, covering core services, storage, big data, machine learning, and networking, with labs and case studies to boost your cloud skills and opportunities.
Save time by consolidating basic Google Cloud concepts into one foundation course, covering commonalities across certifications and paths from cloud architect to machine learning engineer for mastery.
Course Structure
Learn how to obtain a completion certificate on Udemy by ensuring all checkboxes are checked and your progress is complete, then view or download the certificate and receive the email.
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.
Please download attached file and go through LAB Exercise.
Lab Exercise is not tracked systematically and optional. Make sure you mark this lecture complete for your course completion certificate gets generated.
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.
2.2 Planning and configuring compute resources. Considerations include:
Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Kubernetes Engine, App Engine).
Using preemptible VMs and custom machine types as appropriate.
4.1 Managing Compute Engine resources. Tasks include:
Managing a single VM instance (e.g., start, stop, edit configuration, or delete an instance).
SSH/RDP to the instance.
Attaching a GPU to a new instance and installing CUDA libraries.
Viewing current running VM Inventory (instance IDs, details).
Working with snapshots (e.g., create a snapshot from a VM, view snapshots, delete a snapshot).
Working with Images (e.g., create an image from a VM or a snapshot, view images, delete an image).
Working with Instance Groups (e.g., set auto scaling parameters, assign instance template, create an instance template, remove instance group).
Working with management interfaces (e.g., Cloud Console, Cloud Shell, GCloud SDK).
4.2 Managing Kubernetes Engine resources. Tasks include:
Viewing current running cluster inventory (nodes, pods, services).
Browsing the container image repository and viewing container image details.
Working with nodes (e.g., add, edit, or remove a node).
Working with pods (e.g., add, edit, or remove pods).
Working with services (e.g., add, edit, or remove a service).
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK).
4.3 Managing App Engine resources. Tasks include:
Adjusting application traffic splitting parameters.
Setting scaling parameters for autoscaling instances.
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK).
4.4 Managing data solutions. Tasks include:
Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Datastore, Cloud Bigtable, Cloud Dataproc).
Estimating costs of a BigQuery query.
Backing up and restoring data instances (e.g., Cloud SQL, Cloud Datastore, Cloud Dataproc).
Reviewing job status in Cloud Dataproc or BigQuery
Moving objects between Cloud Storage buckets.
Converting Cloud Storage buckets between storage classes.
Setting object lifecycle management policies for Cloud Storage buckets.
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK).
4.5 Managing networking resources. Tasks include:
Adding a subnet to an existing VPC.
Expanding a CIDR block subnet to have more IP addresses.
Reserving static external or internal IP addresses.
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK).
4.6 Monitoring and logging. Tasks include:
Creating Stackdriver alerts based on resource metrics.
Creating Stackdriver custom metrics.
Configuring log sinks to export logs to external systems (e.g., on premises or BigQuery).
Viewing and filtering logs in Stackdriver.
Viewing specific log message details in Stackdriver.
Using cloud diagnostics to research an application issue (e.g., viewing Cloud Trace data, using Cloud Debug to view an application point-in-time).
Viewing Google Cloud Platform status.
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK).
5. Configuring access and security
5.1 Managing Identity and Access Management (IAM). Tasks include:
Viewing account IAM assignments.
Assigning IAM roles to accounts or Google Groups.
Defining custom IAM roles.
5.2 Managing service accounts. Tasks include:
Managing service accounts with limited scopes.
Assigning a service account to VM instances.
Granting access to a service account in another project.
5.3 Viewing audit logs for project and managed services.
5.1 Managing Identity and Access Management (IAM). Tasks include:
Viewing account IAM assignments.
Assigning IAM roles to accounts or Google Groups.
Defining custom IAM roles.
5.2 Managing service accounts. Tasks include:
Managing service accounts with limited scopes.
Assigning a service account to VM instances.
Granting access to a service account in another project.
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.
1.1 Designing performant applications and APIs. Considerations include:
Infrastructure as a Service vs. Container as a Service vs. Platform as a Service (e.g., autoscaling implications)
Portability vs. platform-specific design
Evaluating different services and technologies
Operating system versions and base runtimes of services
Geographic distribution of Google Cloud services
Microservices
Defining a key structure for high write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
Session management
Deploying and securing an API with cloud endpoints
Loosely coupled applications using asynchronous Cloud Pub/Sub events
Health checks
Google-recommended practices and documentation
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.
1.2 Designing secure applications. Considerations include:
Applicable regulatory requirements and legislation
Security mechanisms that protect services and resources
Storing and rotating secrets
IAM roles for users/groups/service accounts
HTTPs certificates
Google-recommended practices and documentation
Already Covered as Part of Foundation.
1.3 Managing application data. Tasks include:
Defining database schemas for Google-managed databases (e.g., Cloud Datastore, Cloud Spanner, Cloud Bigtable, BigQuery)
Choosing data storage options based on use case considerations, such as:
Cloud Storage signed URLs for user-uploaded content
Using Cloud Storage to run a static website
Structured vs. unstructured data
ACID transactions vs. analytics processing
Data volume
Frequency of data access in Cloud Storage
Working with data ingestion systems (e.g., Cloud Pub/Sub, Storage Transfer Service)
Following Google-recommended practices and documentation
1.4 Re-architecting applications from local services to Google Cloud Platform. Tasks include:
Using managed services
Using the strangler pattern for migration
Google-recommended practices and documentation
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.
2.1 Setting up your development environment. Considerations include:
Emulating GCP services for local application development
Creating GCP projects
2.2 Building a continuous integration pipeline. Considerations include:
Creating a Cloud Source Repository and committing code to it
Creating container images from code
Developing unit tests for all code written
Developing an integration pipeline using services (e.g., Cloud Build, Container Registry) to deploy the application to the target environment (e.g., development, test, staging)
Reviewing test results of continuous integration pipeline
2.3 Testing. Considerations include:
Performance testing
Integration testing
Load testing
2.4 Writing code. Considerations include:
Algorithm design
Modern application patterns
Efficiency
Agile methodology
3.1 Implementing appropriate deployment strategies based on the target compute environment (Compute Engine, Google Kubernetes Engine, App Engine). Strategies include:
Blue/green deployments
Traffic-splitting deployments
Rolling deployments
Canary deployments
3.2 Deploying applications and services on Compute Engine. Tasks include:
Launching a compute instance using GCP Console and Cloud SDK (gcloud) (e.g., assign disks, availability policy, SSH keys)
Moving a persistent disk to different VM
Creating an autoscaled managed instance group using an instance template
Generating/uploading a custom SSH key for instances
Configuring a VM for Stackdriver monitoring and logging
Creating an instance with a startup script that installs software
Creating custom metadata tags
Creating a load balancer for Compute Engine instances
3.3 Deploying applications and services on Google Kubernetes Engine. Tasks include:
Deploying a GKE cluster
Deploying a containerized application to GKE
Configuring GKE application monitoring and logging
Creating a load balancer for GKE instances
Building a container image using Cloud Build
3.4 Deploying an application to App Engine. Considerations include:
Scaling configuration
Versions
Traffic splitting
Blue/green deployment
3.7 Deploying and implementing networking resources. Tasks include:
Creating an auto mode VPC with subnets
Creating ingress and egress firewall rules for a VPC (e.g., IP subnets, Tags, Service accounts)
Setting up a domain using Cloud DNS
3.9 Managing Service accounts. Tasks include:
Creating a service account with a minimum number of scopes required
Downloading and using a service account private key file
4.1 Integrating an application with Data and Storage services. Tasks include:
Enabling BigQuery and setting permissions on a dataset
Writing an SQL query to retrieve data from relational databases
Analyzing data using BigQuery
Fetching data from various databases
Enabling Cloud SQL and configuring an instance
Connecting to a Cloud SQL instance
Enabling Cloud Spanner and configuring an instance
Creating an application that uses Cloud Spanner
Configuring a Cloud Pub/Sub push subscription to call an endpoint
Connecting to and running a CloudSQL query
Storing and retrieving objects from Google Storage
Publishing and consuming from Data Ingestion sources
Reading and updating an entity in a Cloud Datastore transaction from an application
Using the CLI tools
Provisioning and configuring networks
4.2 Integrating an application with Compute services. Tasks include:
Implementing service discovery in Google Kubernetes Engine, App Engine, and Compute Engine
Writing an application that publishes/consumes from Cloud Pub/Sub
Reading instance metadata to obtain application configuration
Authenticating users by using Oauth2 Web Flow and Identity Aware Proxy
Using the CLI tools
Configuring Compute services network settings (e.g., subnet, firewall ingress/egress, public/private IPs)
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.
4.3 Integrating Google Cloud APIs with applications. Tasks include:
Enabling a GCP API
Using pre-trained Google ML APIs
Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:
batching requests
restricting return data
paginating results
caching results
Using service accounts to make Google API calls
Using APIs to read/write to data services (BigQuery, Cloud Spanner)
Using the Cloud SDK to perform basic tasks
5.1 Installing the logging and monitoring agent
5.2 Managing VMs. Tasks include:
Debugging a custom VM image using the serial port
Analyzing a failed Compute Engine VM startup
Sending logs from a VM to Stackdriver
5.3 Viewing application performance metrics using Stackdriver. Tasks include:
Creating a monitoring dashboard
Viewing syslogs from a VM
Writing custom metrics and creating metrics from logs
Graphing metrics
Using Stackdriver Debugger
Streaming logs from the GCP Console
Reviewing stack traces for error analysis
Setting up log sinks
Viewing logs in the GCP Console
Profiling performance of request-response
Profiling services
Reviewing application performance using Stackdriver Trace and Stackdriver Logging
Monitoring and profiling a running application
5.4 Diagnosing and resolving application performance issues. Tasks include:
Setting up time checks and other basic alerts
Setting up logging and tracing
Setting up resources monitoring
Troubleshooting network issues
Debugging/tracing cloud apps
Troubleshooting issues with the image/OS
Using documentation, forums and Google support
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.
1.1 Designing a solution infrastructure that meets business requirements. Considerations include:
business use cases and product strategy
cost optimization
supporting the application design
integration
movement of data
tradeoffs
build, buy or modify
success measurements (e.g., Key Performance Indicators (KPI), Return on Investment (ROI), metrics)
Compliance and observability
Provisioning one or more Stackdriver accounts.
1.2 Designing a solution infrastructure that meets technical requirements. Considerations include:
high availability and failover design
elasticity of cloud resources
scalability to meet growth requirements
1.3 Designing network, storage, and compute resources. Considerations include:
integration with on premises/multi-cloud environments
Cloud native networking (VPC, peering, firewalls, container networking)
identification of data processing pipeline
matching data characteristics to storage systems
data flow diagrams
storage system structure (e.g., Object, File, RDBMS, NoSQL, NewSQL)
mapping compute needs to platform products
1.4 Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:
integrating solution with existing systems
migrating systems and data to support the solution
licensing mapping
network and management planning
testing and proof-of-concept
1.5 Envisioning future solution improvements. Considerations include:
cloud and technology improvements
business needs evolution
evangelism and advocacy
2.1 Configuring network topologies. Considerations include:
extending to on-premise (hybrid networking)
extending to a multi-cloud environment which may include GCP to GCP communication
security
data protection
2.2 Configuring individual storage systems. Considerations include:
data storage allocation
data processing/compute provisioning
security and access management
network configuration for data transfer and latency
data retention and data lifecycle management
data growth management
3.1 Designing for security. Considerations include:
Identity and Access Management (IAM)
Resource hierarchy (organizations, folders, projects)
data security (key management, encryption)
penetration testing
Separation of Duties (SoD)
security controls
Managing customer-supplied encryption keys with Cloud KMS
3.2 Designing for legal compliance. Considerations include:
legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), etc.)
audits (including logs)
certification (e.g., Information Technology Infrastructure Library (ITIL) framework)
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.
5.1 Advising development/operation team(s) to ensure successful deployment of the solution. Considerations include:
application development
API best practices
testing frameworks (load/unit/integration)
data and system migration tooling
5.2 Interacting with Google Cloud using GCP SDK (gcloud, gsutil and bq). Considerations include:
local installation
Google Cloud Shell
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.
1.1 Selecting the appropriate storage technologies. Considerations include:
Mapping storage systems to business requirements
Data modeling
Tradeoffs involving latency, throughput, transactions
Distributed systems
Schema design
1.2 Designing data pipelines. Considerations include:
Data publishing and visualization (e.g., BigQuery)
Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
Online (interactive) vs. batch predictions
Job automation and orchestration (e.g., Cloud Composer)
1.3 Designing a data processing solution. Considerations include:
Choice of infrastructure
System availability and fault tolerance
Use of distributed systems
Capacity planning
Hybrid cloud and edge computing
Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
At least once, in-order, and exactly once, etc., event processing
1.4 Migrating data warehousing and data processing. Considerations include:
Awareness of current state and how to migrate a design to a future state
Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
Validating a migration
2.1 Building and operationalizing storage systems. Considerations include:
effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
storage costs and performance
lifecycle management of data
2.2 Building and operationalizing pipelines. Considerations include:
data cleansing
batch and streaming
transformation
data acquisition and import
Integrating with new data sources
3.1 Leveraging pre-built ML models as a service. Considerations include:
ML APIs (e.g., Vision API, Speech API)
customizing ML APIs (e.g., AutoML Vision, Auto ML text)
conversational experiences (e.g., Dialogflow)
3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:
Machine Learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Impact of dependencies of machine learning models
Common sources of error (e.g., assumptions about data)
4.1 Designing for security and compliance. Considerations include:
identity and access management (e.g., Cloud IAM)
data security (encryption, key management)
ensuring privacy (e.g., Data Loss Prevention API)
legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
4.2 Ensuring scalability and efficiency. Considerations include:
building and running test suites
pipeline monitoring (e.g., Stackdriver)
assessing, troubleshooting, and improving data representations and data processing infrastructure
resizing and autoscaling resources
4.3 Ensuring reliability and fidelity. Considerations include:
performing data preparation and quality control (e.g., Cloud Dataprep)
verification and monitoring
planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
choosing between ACID, idempotent, eventually consistent requirements
4.4 Ensuring flexibility and portability. Considerations include:
mapping to current and future business requirements
designing for data and application portability (e.g., multi-cloud, data residency requirements)
Data staging, cataloging and discovery
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.
Explore the Google Cloud professional machine learning engineer certification syllabus across six sections, from architecting low code ml solutions to monitoring models in production, including data collaboration and ml pipelines.
Section 1: Architecting low-code AI solutions (13% of the exam)
1.1 Developing ML models by using BigQuery ML. Considerations include:
● Building the appropriate BigQuery ML model (e.g., linear and binary classi cation, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem
● Feature engineering or selection by using BigQuery ML
● Generating predictions by using BigQuery ML
1.2 Building AI solutions by using ML APIs or foundational models. Considerations include:
● Building applications by using ML APIs from Model Garden
● Building applications by using industry-speci c APIs (e.g., Document AI API, Retail API)
● Implementing retrieval augmented generation (RAG) applications by using Vertex AI Agent Builder
1.3 Training models by using AutoML. Considerations include:
● Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workfows on AutoML)
● Using available data (e.g., tabular, text, speech, images, videos) to train custom models
● Using AutoML for tabular data
● Creating forecasting models by using AutoML
● Configuring and debugging trained models
1.1 Developing ML models by using BigQuery ML. Considerations include:
● Building the appropriate BigQuery ML model (e.g., linear and binary classication, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem
● Feature engineering or selection by using BigQuery ML
● Generating predictions by using BigQuery ML
Explore BigQuery ML prediction and inference with Ml.predict, forecasting using ARIMA+ models, anomaly detection, and recommendations via matrix factorization, plus model building and generating predictions.
Build apps with pre-trained ML APIs—vision, natural language, speech, and translation—to gain inference without training, including text detection, landmark and logo recognition, and sentiment analysis.
Explore retail AI and document AI methods in Google Cloud, including catalog-driven recommendations, clickstream-based personalization, and document processing with custom, general, and specialized processors.
Section 2: Collaborating within and across teams to manage data and models (~14% of the exam)
2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:
● Organizing different types of data (e.g., tabular, text, speech, images, videos) for e cient training
● Managing datasets in Vertex AI
● Data preprocessing (e.g., Data ow, TensorFlow Extended [TFX], BigQuery)
● Creating and consolidating features in Vertex AI Feature Store
● Privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI]) 2
● Ingesting different data sources (e.g., text documents) into Vertex AI for inference 2.2 Model prototyping using Jupyter notebooks. Considerations include: ● Choosing the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, Colab Enterprise, notebooks on Dataproc)
● Applying security best practices in Vertex AI Workbench ● Using Spark kernels ● Integrating code source repositories ● Developing models in Vertex AI Workbench by using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX) ● Leveraging a variety of foundational and open-source models in Model Garden 2.3 Tracking and running ML experiments. Considerations include: ● Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeow Pipelines, Vertex AI TensorBoard with TensorFlow and PyTorch) given the framework ● Evaluating generative AI solutions
Apply security best practices for Vertex AI with encryption, access controls, and private networking under the shared responsibility model. Enforce data residency and Private Service Connect to prevent data exfiltration.
Learn to train models with structured and unstructured data, including images and CSV, ingest via BigQuery or Cloud Storage, and use distributed training with mirror strategies and accelerators.
3.1 Building models. Considerations include:
● Choosing ML framework and model architecture
● Modeling techniques given interpretability requirements
3.2 Training models. Considerations include:
● Organizing training data (e.g., tabular, text, speech, images, videos) on Google Cloud (e.g., Cloud Storage, BigQuery)
● Ingestion of various le types (e.g., CSV, JSON, images, Hadoop, databases) into training
● Training using di erent SDKs (e.g., Vertex AI custom training, Kubeow on Google Kubernetes Engine, AutoML, tabular work ows)
● Using distributed training to organize reliable pipelines
● Hyperparameter tuning
● Troubleshooting ML model training failures
● Fine-tuning foundational models (e.g., Vertex AI, Model Garden)
5.1 Developing end-to-end ML pipelines. Considerations include:
● Data and model validation
● Ensuring consistent data pre-processing between training and serving
● Hosting third-party pipelines on Google Cloud (e.g., MLFlow)
● Identifying components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run) ● Orchestration framework (e.g., Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer)
● Hybrid or multicloud strategies
● System design with TFX components or Kubeflow DSL (e.g., Data ow) 4
5.2 Automating model retraining. Considerations include:
● Determining an appropriate retraining policy
● Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins) 5.3 Tracking and auditing metadata. Considerations include:
● Tracking and comparing model artifacts and versions (e.g., Vertex AI Experiments, Vertex ML Metadata)
● Hooking into model and dataset versioning
● Model and data lineage
Identify risks in ML solutions and monitor security and responsible AI practices, while assessing readiness, biases, fairness, and explainability on Vertex AI, plus drift and performance monitoring.
Explore monitoring, testing, and troubleshooting ml solutions with continuous evaluation metrics, training and serving skew, and feature attribution drift, using Vertex AI to detect prediction drift and surface actionable insights.
1.1 Designing the overall network architecture. Considerations include:
Failover and disaster recovery strategy
Options for high availability
DNS strategy (e.g., on-premises, Cloud DNS, GSLB)
Meeting business requirements
Choosing the appropriate load balancing options
Optimizing for latency (e.g., MTU size, caches, CDN)
Understanding how quotas are applied per project and per VPC
Hybrid connectivity (e.g., Google private access for hybrid connectivity)
Container networking
IAM and security
SaaS, PaaS, and IaaS services
Microsegmentation for security purposes (e.g., using metadata, tags)
Explore how a content delivery network caches static content at Google Cloud CDN edge locations to optimize latency and reduce backend load.
Understand the distinction between project quota and VPC quota, how quotas differ from hard limits, and how to request increases to avoid bill shock.
1.3 Designing a hybrid network. Considerations include:
Using Interconnect (e.g., dedicated vs. partner)
Peering options (e.g., direct vs. carrier)
IPsec VPN
Cloud Router
Failover and disaster recovery strategy (e.g., building high availability with BGP using cloud router)
Shared vs. standalone VPC Interconnect access
Cross-organizational access
Bandwidth
2.1 Configuring VPCs. Considerations include:
Configuring GCP VPC resources (CIDR range, subnets, firewall rules, etc.)
Configuring VPC Peering
Creating a shared VPC and explaining how to share subnets with other projects
Configuring API access (Private, Public, NAT GW, Proxy)
Configuring VPC flow logs
Configure VPC peering between auto network and the customer network, establish the connection, and enable traffic between the two networks by opening firewall rules.
Configure shared vpc by linking a G Suite organization, assigning a network administrator, and sharing subnets with service projects to enable cross-project networking.
2.2 Configuring routing. Tasks include:
Configuring internal static/dynamic routing
Configuring routing policies using tags and priority
Configuring NAT (e.g., CloudNAT, instance-based NAT)
2.3 Configuring and maintaining Google Kubernetes Engine clusters. Considerations include:
VPC-native Clusters using Alias IPs
Clusters with Shared VPC
Private Clusters
Cluster Network policy
Adding authorized networks for Cluster Master Access
Configure and maintain Google Kubernetes Engine clusters by managing Shared VPC configurations, private clusters, and network policies, including alias IP, private Google access, and master authorized networks for controlled access.
2.4 Configuring and managing firewall rules. Considerations include:
Target network tags and service accounts
Priority
Network protocols
Ingress and egress rules
Firewall logs
3.1 Configuring load balancing. Considerations include:
Creating backend services
Firewall and security rules
HTTP(S) load balancer: including changing URL maps, backend groups, health checks, CDN, and SSL certs
TCP and SSL Proxy Load Balancers
Network load balancer
Internal load balancer
Session affinity
Capacity scaling
3.2 Configuring Cloud CDN. Considerations include:
Enabling and disabling Cloud CDN
Using cache keys
Cache invalidation
Signed URLs
3.3 Configuring and maintaining Cloud DNS. Considerations include:
Managing zones and records
Migrating to Cloud DNS
DNS Security (DNSSEC)
Global serving with Anycast
Cloud DNS
Internal DNS
Integrating on-premises DNS with GCP
3.4 Enabling other network services. Considerations include:
Health checks for your instance groups
Canary (A/B) releases
Distributing backend instances using regional managed instance groups
Enabling private API access
4.1 Configuring Interconnect. Considerations include:
Partner (e.g., Layer 2 vs. Layer 3 connectivity)
Virtualizing using Vlan attachments
Bulk storage uploads
4.2 Configuring a site-to-site IPsec VPN (e.g., route-based, policy-based, dynamic or static routing).
4.3 Configuring Cloud Router for reliability.
4.2 Configuring a site-to-site IPsec VPN (e.g., route-based, policy-based, dynamic or static routing).
5.1 Configuring Identity and Access Management (IAM). Tasks include:
Viewing account IAM assignments
Assigning IAM roles to accounts or Google Groups
Defining custom IAM roles
Using pre-defined IAM roles (e.g., network admin, network viewer, network user)
5.2 Configuring Cloud Armor policies. Considerations include:
IP-based access control
6.2 Managing and maintaining security. Considerations include:
Firewalls (e.g., cloud-based, private)
Diagnosing and resolving IAM issues (shared VPC, security/network admin)
6.3 Maintaining and troubleshooting connectivity issues. Considerations include:
Identifying traffic flow topology (e.g., load balancers, SSL offload, network endpoint groups)
Draining and redirecting traffic flows
Cross-connect handoff for Interconnect
Monitoring ingress and egress traffic using flow logs
Monitoring firewall logs
Managing and troubleshooting VPNs
Troubleshooting Cloud Router BGP peering issues
6.4 Monitoring, maintaining, and troubleshooting latency and traffic flow. Considerations include:
Network throughput and latency testing
Routing issues
Tracing traffic flow
7.1 Optimizing traffic flow. Considerations include:
Load balancer and CDN location
Global vs. Regional dynamic routing
Expanding subnet CIDR ranges in service
Accommodating workload increases (e.g., autoscaling vs. manual scaling)
7.2 Optimizing for cost and efficiency. Considerations include:
Cost optimization (Network Service Tiers, Cloud CDN, autoscaler (max instances))
Automation
VPN vs. Interconnect
Bandwidth utilization (e.g., kernel sys tuning parameters)
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Updates in April 2024:
Updated Services Video Content: Google has updated the video content for several of its Cloud Platform services, including Compute Engine, Kubernetes Engine, and Cloud Storage. The updated videos provide more comprehensive and up-to-date information on how to use these services.
Added DevOps Engineer and Cloud Security Engineer Certifications: Google has added two new certifications to its Cloud Platform certification program: the Google Cloud Certified - Professional DevOps Engineer and the Google Cloud Certified - Professional Cloud Security Engineer. These certifications demonstrate that professionals have the skills and knowledge necessary to design, implement, and manage DevOps and Cloud Security solutions on the Google Cloud Platform.
The majority of IT professionals around the world hold at least one certification. The Global Knowledge 2024 IT Skills and Salary Report found that 85% of IT professionals hold at least one certification and that 66% of these professionals intend to acquire a new certification this year.
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Why pay for certification training for GCP.? This course will help you to pay only once and plan for any certification on the Google Cloud Platform.
The course is structured into sections that cover all the concepts in one go, followed by a focused approach toward earning each certification incrementally.
This course will fully prepare you for the certifications listed below.
Cloud Digital Leader
Associate Cloud Engineer
Professional Cloud Architect
Professional Cloud Developer
Professional DevOps Engineer
Professional Security Engineer
Professional Machine Learning Engineer - Exam recap still pending
Professional Cloud Network Engineer - Syllabus changed and don't refer it for actual exam.
Note : Data Engineer is not part of this course.
We have not provided Question Sets for all certifications but updating Exam Recap for you to review your understanding.
This course is not for Lazy learners - This course provides in-depth knowledge of the Google Cloud Platform and requires students to maintain a high level of focus and attention throughout.
For Advanced Professionals joining this course only for certification -> It is recommended that you go through the foundation section at least once to gain a clear understanding of the certification sections.
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