Google Cloud Certification (ACE and PCA)-2023

Explore a comprehensive Google Cloud cheat sheet outlining compute, storage, database, data analytics, and ai/ml services with color codes and links to official documentation for the associate cloud engineer journey.

Explore Google Cloud compute services, including compute engine, app engine, container engine (gke), cloud run, and cloud function, and compare serverless versus customizable infrastructure.

Create a Linux machine in Google Cloud using Compute Engine, choose region and zone, select an E2 micro, then install Apache to host a simple web page via external IP.

Resolve port 22 ssh login errors by creating a default network firewall rule that allows tcp port 22, then reattempt ssh to the vm to verify access.

Create a Windows VM on Google Cloud using a VPC and firewall rules, log in with RDP, and attach a new disk on the fly.

Navigate Google Cloud Shell and gcloud CLI to manage VM instances, projects, and storage, using pre-installed tools, code editor, and Cloud SDK for online development.

Explore how instance templates define VM configurations, including boot disk, OS, labels, and tags, to rapidly create identical VMs and managed instance groups.

Explore Google Cloud instance groups, including managed and unmanaged types, that enable auto scaling, auto healing, health checks, load balancing, and rolling updates.

Learn to perform rolling updates on a managed instance group, upgrading from version 1 to 2 via templates and a load balancer, using gradual rollout and proactive automatic upgrades.

Explore Google App Engine, a fully managed serverless platform for deploying web applications, with standard and flexible environments, multi-service and multi-version support, and hands-on deployment of a Python Flask app.

Split traffic between app engine versions to test new features, migrate to version three after validation, and monitor via deployment and logs, while managing instances and firewall rules.

Discover Google Kubernetes Engine basics, including container orchestration with Kubernetes, architecture with master and worker nodes, pods, API server, scheduler, and deployment on Google Cloud.

Enable the Kubernetes engine API, create a zonal GKE cluster with three nodes, build a Python Flask app, containerize it, push to GCR, deploy, and expose the service.

Learn to containerize a simple Python Flask app, build and push a Docker image, and deploy it on Google Kubernetes Engine with a load-balanced service.

Interact with GKE using kubectl cli hands-on by connecting from cloud shell, listing deployments, pods, and services, deploying workloads, exposing services, and scaling deployments while resizing the cluster.

Learn how a lightweight serverless cloud function uses event-based storage triggers to check file size and move large uploads to a destination bucket, leaving smaller files untouched.

Explain the difference between internal and external IPs in GCP, how private ranges assign internal addresses, and how external IPs enable internet access and inter-VPC communication via peering.

Learn to create a custom VPC with a subnet in Asia South 1, deploy two VM instances, and configure firewall rules and tags to allow SSH access and control traffic.

Learn to set up VPC peering between two VPCs, enabling private IP communication and reduced latency by keeping traffic inside the Google Cloud network, demonstrated live.

Learn to design a multi-project network with a shared VPC, using a host project and attached service projects. Assign run permissions and deploy a VM in the shared network.

Create a shared VPC by using a host and a service project within an organization, then grant the Compute VPC Shared admin role and enable the Compute Engine API.

Learn to access internet from a private vm without external ip by configuring a cloud net with a nat gateway in a demo vpc.

Explore how Google Cloud load balancers distribute traffic to the nearest healthy instance to minimize latency, and learn to create backend services and configure external and internal load balancers.

See how a Google Cloud HTTP load balancer fronts a managed instance group, routing users to the nearest healthy instance across zones to prevent downtime and support auto scaling.

Explore Google Cloud Storage services, including object storage with GCS buckets, block storage with persistent disks, relational and NoSQL databases, and in memory stores with BigQuery for data warehousing.

Explores Google Cloud Storage buckets, storage classes (standard, nearline, coldline, archive), regional choices, and protections like object versioning, retention policy, and lifecycle rules for data safety.

Master Google Compute Engine disks and snapshots, including boot and data disks, attaching and mounting, and using incremental backups with cross-zone and cross-region restoration.

Explore Google Cloud local SSD, a physically attached, fixed 375 GB drive that delivers high IOPS and is not compatible with the E2 machine type.

launch and configure a Cloud SQL instance for MySQL, PostgreSQL, or SQL Server in the Mumbai region; enable api, set backups, and connect via Cloud Shell and MySQL Workbench.

Explore Cloud Spanner, a fully managed relational database with global scale, transactional consistency, and automatic synchronous replication, via regional and multi regional deployments and hands-on setup.

Discover Google Cloud Bigtable, a fully managed NoSQL database for time series, IoT, marketing, financial, and graph data, with use cases and the CBT command line tool.

Discover Google Cloud Bigtable as a no SQL database with low latency and replication, and learn to create an instance, table with column families, and CBT backup and restore.

Explore Google Cloud BigQuery, a fully managed serverless enterprise data warehouse that enables storage, analysis, and insights with built-in machine learning, columnar storage, geospatial analysis, and business intelligence.

Create a data set and a table in BigQuery, upload a CSV, and run queries while examining syntax checks, caching, and cost optimization through column selection.

Learn how to boost BigQuery query performance with partitioning, using a hands-on demo to create a partitioned table from Tech Forum data and compare scans and costs.

Create external tables from cloud storage like gcs, s3, and Azure, then run federated queries by connecting BigQuery to Cloud Spanner using external_query.

Explore Google Cloud billing basics, view current month charges, and export billing data to BigQuery to build a Data Studio dashboard analyzing costs by service.

Explore key data processing and ETL in Google Cloud, from ingestion to analytics, covering Dataflow, Data Fusion, Dataproc, storage options, and tools like BigQuery, Looker, and Vertex AI.

Google Cloud Pub/Sub is a synchronous publish/subscribe messaging service built on Google core infrastructure, enabling reliable and scalable real-time streaming data processing with topics, publishers, subscriptions, and acknowledgments.

Explore Google Cloud dataflow, a unified, serverless Apache Beam platform for stream and batch pipelines, with in-built templates and examples using Spanner, GCS, Pub/Sub, and BigQuery.

Create a Dataflow batch job using a Google Cloud utility template to compress csv files from a storage bucket into a zip folder, with a failed folder and tmp.

Build a dataflow job using SQL queries in BigQuery, selecting Europe region data and creating a Europe sales table via Dataflow SQL editor.

Cloud Data Fusion offers a code-free, drag-and-drop, fully managed data integration service on Google Cloud. Build and transform pipelines via a UI with extensive connectors.

build a data pipeline with cloud data fusion to apply transformations—delete a column, filter offline sales, mask total profit, and uppercase country—and load into BigQuery.

Explore Google Cloud Composer, a fully managed Apache Airflow service, to author, schedule, and monitor pipelines with DAGs and tasks across GCP services.

Learn to create a Google Cloud Composer environment by provisioning GKE cluster, Apache Airflow image, and storage bucket, using a service account to enable permissions, logging, and monitoring across GCP.

Explore cloud composer architecture, including cloud sql, GKE cluster, airflow components, and GCS buckets, and understand tenant project deployment and cloud sql proxy integration.

Create your first dag in Google Cloud Composer by writing a Python script that defines the dag and its tasks, then upload it to the decks folder.

learn how to create a data studio dashboard from a BigQuery employee table, build a two-page report with filters, charts, and a scheduled, shareable export.

Create a log sink to export logs to a BigQuery table by setting up a data set and configuring a log router destination.

Learn to create alerts for GPM services and monitor a GCP VM via cloud monitoring, including installing the ops agent, verifying Apache health, and configuring email notifications and incidents.

Explore container registry versus artifact registry in Google Cloud: container registry stores docker images; artifact registry stores docker images plus OS and language packages with regional repositories and access control.

create a simple web application, write a Dockerfile, build a Docker image, and push it to container registry and artifact registry.

Explore cloud source repositories and cloud build in Google Cloud to automate builds with a private git repository, Docker images, and build triggers.

Discover Google Cloud AI/ML services using cloud console, cloud shell, or Python; learn pre-trained models and custom training with vision API, speech API, text to speech, natural language, and video intelligence APIs.

Take a hands-on look at the vision API as a pre-trained model, detecting faces, objects, labels, and text, with confidence scores, via cloud shell and Python.

Explore Google Cloud's natural language processing API with hands-on demos of sentiment analysis, entity and syntax detection from unstructured text, using Python and cloud shell.

Upload a video to a cloud storage bucket and use the video intelligence api to detect explicit content, confirming the video is safe across time frames.

Create and label a face dataset with names, train a Vertex AI model, and deploy an endpoint to recognize people by name or classify sports.

Prepare for the associate cloud engineer exam by mastering core compute, storage, and networking concepts, with hands-on GCP practice and review of patterns and mock questions using gcloud.

Learn how to prepare for the professional cloud architect exam by understanding GCP certification levels, exam pattern with case studies, and best-practice, cost-efficient, highly available architectures using Google Cloud documentation.