GCP Associate Cloud Engineer–Google Cloud Certification Prep

Explore how Google Cloud Platform's Compute Engine provides scalable infrastructure as a service with customizable virtual machines, featuring CPU, memory, storage, region, and zone.

Navigate to compute engine, create a virtual machine instance with a chosen operating system image, adjust memory and vcpus, set labels, and then create a snapshot from the source disk.

Master advanced vm creation options, including networking (ip forwarding, vpc, interface counts), disk and encryption choices, security (shielded vms, ssh keys), management (startup scripts, metadata, reservations), and sole tenancy.

Learn how to add GPUs to a Google Cloud VM, choose Nvidia A100 options, configure ultra GPU machines, and review host maintenance termination policies.

Learn to install the Google Cloud SDK and use the gcloud CLI to create and manage VM instances, configure projects, zones, disks, and machine types across Windows, macOS, and Linux.

Activate Cloud Shell to run gcloud commands for managing compute instances. Learn to authorize and list instances in your project using gcloud compute instances list.

Create instance groups from an instance template, choosing managed or unmanaged types with load balancing and autoscaling for high availability across zones; managed groups support automated updates and auto healing.

Create an instance template with gcloud compute, then use it to build a managed instance group in cloud shell with zones and auto scaling driven by cpu utilization at 60%.

Deploy a hello world app on Google Compute Engine by creating a low-cost E2 instance, enabling web traffic, installing nginx, and configuring index.html.

Explore the shift from monolithic to microservice architectures and learn how Kubernetes Engine automates the management, scaling, and deployment of microservice apps for improved scalability and fault isolation.

Learn how Google Kubernetes Engine automates container orchestration with a cluster of master and worker nodes, pods, and Docker containers, enabling auto scaling, rolling updates, and seamless portability across clouds.

Learn to create an autopilot Kubernetes cluster in the Google Cloud Console, compare autopilot and standard clusters, and configure networking, public and private endpoints, and pod and service address ranges.

Learn how to create a standard GKE cluster from the console, choosing zonal or regional settings, configuring node pools, release channels, auto scaling, and essential networking and security options.

Build and deploy a hello world Python Flask app to Google Kubernetes Engine by creating a Docker image, pushing it to Artifact Registry, and exposing it with a service.

Explore how to use deployment and service yaml files to define, deploy, and manage apps on Google Kubernetes Engine, including replicas, containers, selectors, and load balancer exposure.

Scale a deployment in Google Kubernetes Engine using horizontal and vertical pod autoscalers, configure min and max replicas, and tune CPU and memory metrics and resource requests to balance load.

Learn how to clean up Google Kubernetes Engine resources to prevent charges. Delete deployments, services, clusters, artifact registry, images, and build directories to complete the cleanup.

Explore Cloud Run, a fully managed serverless platform that runs containerized apps and auto-scales, charging only for resources used. Deploy services and short-lived jobs with container images and https endpoints.

Deploys an app to Cloud Run by creating a service or a short‑lived job, selecting an existing container image from artifact registry, and configuring CPU, autoscaling, ingress, and authentication.

Explore Google Cloud's App Engine, a platform as a service that auto-scales web applications with standard and flexible environments, supports Python, Node.js, Java, PHP, and deploys via gcloud app deploy.

Create and deploy a hello world app to App Engine using a config file app.yaml, main.py, and Gunicorn with Python 3.8, then deploy with gcloud app deploy and browse.

Learn how cloud functions deliver serverless, event-driven code that runs in response to http requests, storage changes, and Eventarc triggers, with pay-per-use pricing and scalable execution.

Create a Google Cloud function triggered by Google Cloud Storage events in a function bucket to rename an uploaded file via a rename file entry point and deploy with package.json.

Explore artifact registry and container registry, learn how CI/CD pipelines build, store, and deploy Docker images, and push and pull artifacts to a regional artifact registry in GCP.

Learn how Anthos provides a unified cloud operating model to manage multi-cluster Kubernetes across on-premises and cloud, with Istio service mesh, config management, and centralized governance.

Learn how Anthos orchestrates deployments on Google Cloud using GKE, Artifact Registry, and Cloud Build, with fleet registration, config management, Istio service mesh, and Cloud Run for Anthos.

Master Google Cloud Storage basics: buckets and objects, storage classes with auto class, uniform versus fine-grained access, public access prevention, and versioning plus retention policies.

Explore relational databases in GCP with Cloud SQL, compare MySQL, PostgreSQL, and SQL Server engines, and understand primary and foreign keys, ACID compliance, and use cases.

Navigate Cloud SQL to create a MySQL instance, configure resources, connect via Cloud Shell, create a database and a table, run queries, and delete the instance.

Explore AlloyDB, a fully managed PostgreSQL-compatible database in GCP that disaggregates compute and storage for scalable, high-performing enterprise workloads, with failover, replication, and customer-managed encryption keys via VPC service controls.

Create and deploy an Alloydb cluster, configure the network, and connect via a virtual machine instance to install the PostgreSQL client, then create a database, a table, and insert data.

Explore Cloud Spanner, a fully managed, scalable relational database that provides strong cross-region consistency, horizontal growth, standard SQL, and seamless Google Cloud integration for global transactional workloads.

Navigate to cloud spanner, create a provisioned regional instance with processing units, then build a database, add an employees table, and run queries via spanner studio.

Explore NoSQL databases in GCP, comparing document, key-value, column, and graph models, with Firestore and Datastore, real-time updates, offline support, and schema flexibility.

Explore Cloud Bigtable, a highly scalable NoSQL wide-column database, and learn to create an instance, configure storage, region, and scaling, connect via CBT CLI, and manage tables and data.

Explore Cloud Memorystore, a fully managed in-memory Redis-based store for caching, sessions, and real-time analytics. Create basic or standard instances, secure with IAM and auth, and connect via Redis protocol.

Compare GCP database services—Cloud SQL, Alloydb, Cloud Spanner, Firestore, Bigtable, and Memorystore—and choose Cloud Spanner for globally distributed, strongly consistent, low-latency real-time multiplayer games.

Learn how to create and configure a Virtual Private Cloud (VPC) in Google Cloud, including subnets, IPv4/IPv6 dual stack, firewall rules, Private Google Access, and VPC flow logs.

Understand hybrid connectivity in Google Cloud Platform and configure VPN options, including high availability and classic VPN, to link on premises networks with a VPC.

Explore hybrid connectivity with cloud interconnect, enabling private high-bandwidth, low-latency links between on-premises data centers and GCP for secure data transfer and workload migration.

Explore Cloud DNS, Google's scalable domain name system that maps domain names to IP addresses, enabling ownership verification, and public or private zones with DNSSEC and efficient DNS records management.

Explore external and internal cloud load balancing, using http(s) routing and a global anycast vip to distribute traffic to healthy back-end resources for improved performance and availability.

Explore how Cloud CDN caches content on edge servers to reduce latency, serving content from back end bucket, custom origin, or back end service with load balancing and caching rules.

Explore the basics of identity and access management (IAM) in Google Cloud, including policies, bindings, roles, principals, and service accounts, plus best practices for least privileges and auditing.

Discover how Cloud Armor uses security policies and rule sets to allow or deny traffic by ip address and geographic location at the network edge, protecting apps from DDoS.

Explore Google Cloud Platform's cloud firewall, a network-level security tool that controls VM traffic with rules based on source and destination IPs, protocols, and ports.

Explore how the cloud security command center provides centralized security management and data risk assessment for GCP resources, delivering visibility into assets, vulnerability scanning, threat detection, and compliance.

Explore BigQuery, a fully managed serverless data warehouse that enables fast real-time analytics with SQL-like queries using joins and aggregations across employees, departments, and salaries.

Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow that lets you author, schedule, and monitor data pipelines as code, using DAGs to define task dependencies.

Explore how Cloud Dataflow enables fully managed batch and streaming data processing through pipelines of transformations, from sources like Cloud Storage and BigQuery to output sinks and job templates.

Explore Cloud Dataproc, a managed service that simplifies deploying and managing Hadoop, Spark, Hive, and Pig on on-demand clusters, enabling fast data analysis and scalable processing.

Cloud Dataprep offers a no-code, visual interface to ingest data from diverse sources, clean, structure, and enrich it, profile quality, apply transformations, and export to databases or data warehouses.

Google Cloud Data Fusion provides a code-free, graphical platform to design, deploy, and manage ETL pipelines that integrate data from diverse sources into BigQuery, with Dataproc-backed scaling.

Explore Google Cloud Pub/Sub, a publish and subscribe messaging service that uses topics and subscriptions to enable real-time, event-driven communication between publishers and subscribers.

Choose the right analytics service by weighing data volume, real-time or batch needs, complexity, and coding skills, using BigQuery, Dataflow, Dataproc, Dataprep, Data Fusion, Pub Sub, and Cloud Composer.

Explore Vertex AI, a unified machine learning platform on GCP that combines notebooks, pipelines, training, AutoML, feature store, predictions, and deployment to build, train, evaluate, and monitor models at scale.

Learn how Vertex AI Vision enables image and video analysis with pre-trained and AutoML vision models, supporting object detection, text recognition, and batch, streaming, or online predictions.

Prepare data with BigQuery, Cloud Storage, and Dataflow to train and deploy models using AI platform and Vertex AI. Evaluate with explainable AI and deploy for real time predictions.

Explore Gen App Builder, a no-code platform that enables enterprise-grade generative AI apps with a visual drag-and-drop interface, templates, data integrations, and Google Cloud security.

Learn how the speech-to-text tool converts audio to text, adds punctuation and capitalization, supports real-time and stored files, multiple languages, and speaker diarization.

Master Google Cloud Monitoring by capturing metrics, building dashboards, and configuring alerting policies while integrating with logging, the ops agent, and uptime checks.

Learn to collect, store, search, and analyze logs with Google Cloud Logging, use Log Explorer for filtering, and export logs to BigQuery for advanced analysis.

Explore how Google Cloud Trace monitors and profiles applications by capturing traces and spans to identify latency, bottlenecks, and performance optimizations across App Engine, Cloud Functions, and Cloud Run.

Google Cloud error reporting centralizes and analyzes errors from App Engine, Cloud Functions, and Kubernetes, automatically collects error data where supported, and helps you configure notifications to improve reliability.

Explore cost optimization in GCP, including pay-as-you-go billing, right sizing, resource tagging, cost allocation, scaling, and budgeting to prevent overruns and maximize efficiency.

Learn how rightsizing in GCP optimizes compute and storage to balance performance and cost by selecting appropriate VM types, monitoring utilization, and resizing resources as workloads change.

Explore cost allocation and budgeting in GCP to track and allocate cloud expenses across projects, departments, and services, set budgets with alerts, and enable chargebacks using historical data.

Estimate and optimize cloud costs with the GCP pricing calculator by configuring compute, storage, networking, and databases, selecting regions, and comparing hourly or monthly estimates.

Apply cost optimization best practices in Google Cloud Platform by right-sizing resources, enabling autoscaling, leveraging sustained and committed use discounts, and using preemptible virtual machines when appropriate.