Google Cloud Certified – Associate Cloud Engineer Course

Master Google Cloud tools with a structured learning path

Well-organised curriculum featuring four core modules

Get prepared for Google Cloud Certified Associate Cloud Engineer exam

Learn from instructors with extensive industry experience

Earn the globally recognised GCP - Associate Cloud Engineer certification

Gain experience through hands-on labs and collaborative projects

Flexible learning modes that adapt to your personal schedule

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6595 Enrolled

Overview

What you will learn:

Enable teams to efficiently manage IAM roles and permissions for secure cloud access
Configure projects, accounts and networking on GCP
Build and deploy container apps using GKE clusters
Track expenses and manage GCP billing accounts
Use Kubernetes CLI to deploy cloud-native applications
Design multi-region storage solutions using Cloud Storage
Set up Cloud VPN, load balancers, firewalls

Learning Outcomes

After finishing the course, you will be able to:

Gain expertise in IAM roles to manage secure access in Google Cloud environments

Configure cloud networking with VPN, subnets and routing for secure communication

Deploy scalable containerized apps using GKE and Kubernetes CLI efficiently

Use Cloud Run to implement serverless & event-driven application solutions

Build and manage multi-region storage solutions with Cloud Storage and BigQuery tools

Automate Infrastructure as Code (IaC) management using Terraform scripts and Helm charts

Optimize enterprise cloud operations for secure, scalable, cost-efficient deployments

Ready to get started?

Prerequisites

There are no mandatory requirements to enrol for the Google Cloud Certified Associate Cloud Engineer Course. However, it is strongly recommended that candidates should have the following:

A minimum of six months of practical experience working with Google Cloud Platform (GCP)
A solid understanding of core GCP services, using the GCP Console and Command-Line Interface (CLI)
Experience in deploying and managing virtual machines and platform services

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Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Related courses

Curriculum

1.1 Setting up cloud projects and accounts. Considerations include:

Creating a resource hierarchy
Applying organizational policies to the resource hierarchy
Granting members Identity and Access Management (IAM) roles within a project
Managing users and groups in Cloud Identity (manually and automated)
Enabling APIs within projects
Provisioning and Setting up products in Google Cloud Observability
Assessing quotas and requesting increases
Setting up standalone organizations
Setting up cloud networking
Confirming availability of products in geographical locations (e.g., regional, global)
Configuring Cloud Asset Inventory and using Gemini Cloud Assist to analyze resources

1.2 Managing billing configuration. Considerations include:

Creating one or more billing accounts
Linking projects to a billing account
Establishing billing budgets and alerts
Setting up billing exports

2.1 Planning and implementing compute resources. Considerations include:

Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Google Kubernetes Engine [GKE], Cloud Run, Cloud Run functions, Knative serving)
Launching a compute instance (e.g., availability policy, SSH keys)
Choosing the appropriate storage for Compute Engine (e.g., zonal Persistent Disk, regional Persistent Disk, Google Cloud Hyperdisk)
Creating an autoscaled managed instance group by using an instance template
Configuring OS Login
Configuring VM Manager
Using Spot VM instances and custom machine types
Installing and Configuring the command-line interface (CLI) for Kubernetes (kubectl)
Deploying a GKE cluster with different configurations (e.g., GKE Autopilot, regional clusters, private clusters)
Deploying a containerized application to GKE
Deploying an application to serverless compute platforms, including for the processing of Google Cloud events (e.g., Pub/Sub events, Cloud Storage object change notification events, Eventarc)

2.2 Planning and implementing storage and data solutions. Considerations include:

Choosing and deploying data products (e.g., Cloud SQL, BigQuery, Firestore, Spanner, Bigtable, AlloyDB, Dataow, Pub/Sub, Google Cloud Managed Service for Apache Kaa, Memorystore)
Choosing and deploying storage products (e.g., Cloud Storage, Filestore, Google Cloud NetApp Volumes) and Cloud Storage options (e.g., Standard, Nearline, Coldline, Archive)
Loading data (e.g., command-line upload, load data from Cloud Storage, Storage Transfer Service)
Maintaining multi-region redundancy across data solutions

2.3 Planning and implementing networking resources. Considerations include:

Creating a VPC with subnets (e.g., custom mode VPC, Shared VPC)
Creating and applying Cloud Next Generation Firewall (Cloud NGFW) policies with ingress and egress rules and attributes (e.g., action, source, destination, targets, protocols, ports)
Using Tags (e.g., secure Tags) and service accounts in Cloud NGFW policy rules
Establishing network connectivity (e.g., Cloud VPN, VPC Network Peering, Cloud Interconnect)
Choosing and deploying load balancers
Differentiating Network Service Tiers

2.4 Planning and implementing resources through infrastructure as code. Considerations include:

Infrastructure as code tooling (e.g., Fabric FAST, Cong Connector, Terraform, Helm)
Planning and executing infrastructure as code deployments, including versioning, state management and updates

3.1 Managing compute resources. Considerations include:

Remotely connecting to a Compute Engine instance
Viewing current running Compute Engine instances
Working with snapshots and images (e.g., create, view, and delete images or snapshots; schedule a snapshot)
Viewing current running GKE cluster inventory (e.g., nodes, Pods, Services)
Configuring GKE to access Artifact Registry
Working with GKE node pools (e.g., add, edit, or remove a node pool; autoscaling node pool)
Working with Kubernetes resources (e.g., Pods, Services, StatefulSets)
Managing horizontal and vertical Pod autoscaling configurations
Managing GKE Autopilot Pod resource requests
Deploying new versions of a Cloud Run application
Adjusting application traffic splitting parameters (e.g., Cloud Run, Cloud Run functions, GKE)
Configuring autoscaling for a Cloud Run application

3.2 Managing storage and data solutions. Considerations include:

Managing and securing objects in Cloud Storage buckets
Setting object lifecycle management policies for Cloud Storage buckets
Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Bigtable, Spanner, Firestore, AlloyDB)
Estimating costs of data storage resources
Backing up and restoring database instances (e.g., Cloud SQL, Firestore, Spanner, AlloyDB, Bigtable)
Reviewing job status (e.g., Dataow, BigQuery)
Using Database Center to manage the Google Cloud database fleet

3.3 Managing networking resources. Considerations include:

Adding a subnet to an existing VPC
Expanding a subnet to have more IP addresses
Reserving static external or internal IP addresses
Adding custom static routes in a VPC
Working with Cloud DNS and Cloud NAT

3.4 Monitoring and logging. Considerations include:

Creating Cloud Monitoring alerts based on resource metrics
Creating and ingesting Cloud Monitoring custom metrics (e.g., from applications or logs)
Exporting logs to external systems (e.g., on-premises, BigQuery)
Configuring log buckets, log analytics and log routers
Viewing and ltering logs in Cloud Logging
Viewing specic log message details in Cloud Logging
Using cloud diagnostics to research an application issue (e.g., Cloud Trace, Cloud Profiler, Query Insights, index advisor)
Viewing the Personalized Service Health dashboard
Configuring and deploying Ops Agent
Deploying Google Cloud Managed Service for Prometheus
Configuring audit logs
Using Gemini Cloud Assist for Cloud Monitoring
Using Active Assist to optimise resource utilisation

4.1 Managing Identity and Access Management (IAM). Considerations include:

Viewing and creating IAM policies
Managing the various role types and defining custom IAM roles (e.g., basic, predefined, custom)

4.2 Managing service accounts. Considerations include:

Creating and developing service accounts
Using service accounts in IAM policies with minimum permissions
Assigning service accounts to resources
Managing IAM permissions of a service account
Managing service account impersonation
Creating and managing short-lived service account credentials
Using a Google Cloud service account with a GKE application

Frequently asked questions

The Google Cloud Certified – Associate Cloud Engineer Course is designed to provide you with the skills required to deploy, manage and operate applications and infrastructure on Google Cloud Platform (GCP). Our course serves as a foundational certification for individuals aspiring to build a career in cloud engineering or IT infrastructure management using Google Cloud services.

Yes, this course adds strong value by enhancing your DevOps skills with hands-on experience in GKE, Kubernetes, Cloud Run, Terraform and Helm. Professionals learn to automate infrastructure, streamline CI/CD workflows and manage scalable cloud deployments on Google Cloud. We help you improve efficiency, reliability and performance in production environments.

Our course helps an organization reduce cloud operational costs by training teams to optimize resource usage, implement scalable architectures and automate infrastructure using Google Cloud best practices. Your team will learn efficient workload management, container orchestration with GKE and serverless solutions like Cloud Run, minimizing overprovisioning while improving performance and operational efficiency.

There are no mandatory requirements to enrol for the Google Cloud Certified Associate Cloud Engineer Course. However, it is strongly recommended that candidates should have the following:

1. A minimum of six months of practical experience working with Google Cloud Platform (GCP)
2. A solid understanding of core GCP services, using the GCP Console and Command-Line Interface (CLI)
3. Experience in deploying and managing virtual machines and platform services

In the GCP Associate Cloud Engineer (ACE) Course, you will explore essential topics that prepare you to manage and secure applications and infrastructure on the Google Cloud Platform (GCP). These are the core topics:

1. Google Cloud Platform Overview
2. Setting Up a Cloud Solution Environment
3. Planning and Configuring a Cloud Solution
4. Deploying and Implementing Cloud Solutions
5. Successful Operation of a Cloud Solution
6. Configuring Access and Security
7. Using Google Cloud Storage and Databases
8. Automation and Infrastructure as Code

After completing the GCP – Associate Cloud Engineer (ACE) training course, you can lead to a variety of career opportunities globally. These job roles are:

1. Associate Cloud Engineer
2. Cloud Support Engineer
3. Cloud Systems Administrator
4. DevOps Engineer
5. Cloud Operations Engineer

Earning the Google Cloud Certified – Associate Cloud Engineer (ACE) certification comes with a range of benefits. These benefits are as follows:

1. Ability to manage applications and infrastructure on Google Cloud
2. Career advancement opportunities as Cloud Engineer
3. In-demand cloud skills in GCP
4. Strong foundation for advanced certifications
5. Gain experience on production-grade cloud deployments

Yes, our GCP - ACE course is specifically designed to prepare you for the official Google Cloud Certified – Associate Cloud Engineer certification exam. This is how the course helps you get fully exam-ready:

1. Aligned with exam domains
2. Hands-on labs & real-world scenarios
3. Practice questions & mock exams
4. Gain tips & exam strategies
5. Updates you on the latest exam version