Google Cloud Platform (GCP)

Understand IaaS, PaaS, SaaS models, regions, zones, and how GCP fits into the cloud landscape.

Navigate the GCP console, use Cloud Shell, and manage projects and billing accounts.

Manage identity, roles, service accounts, and understand GCP's organization → folder → project hierarchy.

Use the gcloud command-line tool to create, configure, and manage GCP resources programmatically.

Provision and manage virtual machines, instance groups, and understand machine types and preemptible VMs.

Deploy stateless containers as fully managed serverless services with automatic scaling.

Deploy, manage, and scale containerized applications using managed Kubernetes clusters.

Write lightweight event-driven functions that respond to cloud events without managing servers.

Deploy web applications on a fully managed platform with automatic scaling and zero server management.

Design virtual private clouds, configure subnets, firewall rules, and understand GCP's global networking model.

Configure HTTP(S), TCP, and internal load balancers, and use Cloud CDN for content delivery.

Protect applications with WAF policies, DDoS protection, and security best practices.

Manage DNS zones and records for reliable, low-latency domain name resolution.

Store and retrieve unstructured data using buckets with lifecycle policies, versioning, and access controls.

Use managed relational databases (MySQL, PostgreSQL, SQL Server) with automatic backups and replication.

Build real-time applications with a flexible, scalable NoSQL document database.

Handle massive analytical and operational workloads with a petabyte-scale wide-column NoSQL database.

Use a globally distributed, strongly consistent relational database for mission-critical workloads.

Run fast SQL analytics on petabyte-scale datasets using GCP's serverless data warehouse.

Build batch and streaming data pipelines using managed Apache Beam for ETL and real-time processing.

Decouple services with a fully managed messaging system for event-driven architectures.

Run Apache Spark and Hadoop clusters as a managed service for large-scale data processing.

Automate builds, run tests, and store container images and packages in managed registries.

Define and provision GCP infrastructure declaratively using Terraform with state management.

Observe system health with metrics, dashboards, alerting policies, and centralized log management.

Train, deploy, and manage ML models using GCP's unified AI platform with AutoML and custom training.

Use Vision, Speech, Natural Language, and Translation APIs to add intelligence without building models.

Apply GCP's Well-Architected Framework: reliability, security, cost optimization, and performance.

Control spending with budgets, committed use discounts, and resource right-sizing strategies.