This GCPPCA-E: Preparing for the Professional Cloud Architect Examination course provides basic information about the Professional Cloud Architect certification exam, including sample questions. It’s designed to eliminate any confusion or misunderstandings about the process and nature of the exam itself. You’ll also explore additional training resources that can help you prepare for the exam.
Accelerate cloud expertise in 2026 with Google Cloud certification training.
Google Cloud certifications validate your ability to design, deploy and manage cloud solutions across platforms, services and real-world workloads.
Google Cloud Certified programs help you gain practical knowledge, prepare for industry exams, and develop skills in areas such as cloud fundamentals, development, data analytics, AI, and infrastructure.
- Why get trained: Develop foundational to advanced cloud skills using Google Cloud technologies so you can confidently support cloud initiatives, implement solutions and demonstrate your expertise with recognized certification credentials that employers increasingly seek.
- Why it matters: Cloud capabilities are a core requirement for digital transformation. Professionals with Google Cloud certification help their organizations innovate faster, secure workloads, optimize resources, and make data-driven decisions — giving you a competitive advantage in today’s cloud-driven job market.
- Who should attend: Aspiring cloud professionals, developers, data engineers, solution architects, IT administrators, business analysts, and anyone seeking structured training and certification pathways on Google Cloud — from foundational roles to professional specialization.
Enroll in Google Cloud Certified training and start building the skills and credentials that help you stand out and deliver results in cloud technology roles.
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This Architecting with Google Cloud: Design and Process course features a combination of lectures, design activities, and hands-on labs to show you how to use proven design patterns on Google Cloud to build highly reliable and efficient solutions and operate deployments that are highly available and cost-effective.
This course was created for those who have already completed the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course.
Limited-time offer: 40% off – sharpen your cloud architecture skills for only RM2,880.
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This training course builds on the networking concepts covered in the Networking Fundamentals in Google Cloud course. Through presentations, demonstrations, and labs, participants explore and deploy Google Cloud networking technologies.
These technologies include: Virtual Private Cloud (VPC) networks, subnets, and firewalls; Interconnection among networks; Load balancing ;Cloud DNS; Cloud CDN; Cloud NAT.
The course will also cover common network design patterns.
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Learn how to deploy and manage containerized applications on Google Kubernetes Engine (GKE) and the other tools on Google Cloud. This course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements—including infrastructure components like pods, containers, deployments, and services—along with networks and application services. You’ll also learn how to deploy practical solutions, including security and access management, resource management, and resource monitoring.
Learn more about Kubernetes Expertise here: Kubernetes Expertise: Unlocking Cloud Potential in Malaysia
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This GCPGCE: Architecting with Google Compute Engine course will familiarize you with Google Cloud’s flexible infrastructure and platform services, with a specific focus on Compute Engine. This session uses a combination of lectures, demos, and hands-on labs to explore and deploy solution elements, including infrastructure components like networks, systems, and application services.
You’ll also learn how to deploy practical solutions such as secure interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
Now at RM4,500 – get 37.5% off this hands-on course for aspiring cloud engineers.
Learn more about Multicloud Strategies in Malaysia: Adopting Multicloud Strategies in Malaysia: A 2024 Roadmap
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Data Engineers design solutions that ensure maximum flexibility and scalability, while meeting all required security controls.
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning.
This Google Cloud course covers structured, unstructured, and streaming data.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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Learn about Google Cloud’s computing and storage services available, including Compute Engine, Google Kubernetes Engine, App Engine, Cloud Storage, Cloud SQL, and BigQuery.
This GCPCIN: Google Cloud Fundamentals: Core Infrastructure course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies
Enroll now for RM1,320 – enjoy 45% off this entry-level course in Google Cloud fundamentals.
Learn more about Multicloud Strategies in Malaysia: Adopting Multicloud Strategies in Malaysia: A 2024 Roadmap
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This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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This GCP-AWS: Google Cloud Fundamentals for AWS Professionals course is designed for AWS system administrators, solutions architects, and SysOps administrators who are familiar with AWS features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.
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If you are new to Google Workspace, this training will equip you with the skills you need to be productive in the workplace. Through a series of lectures, demonstrations, and hands-on activities, you will become proficient in the use of the following core Google Workspace applications: Gmail, Google Calendar, Google Drive, Google Docs, Google Sheets, Google Slides, Google Meet and Google Chat.
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What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
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In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.
Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.
