Google Associate Cloud Engineer Certification Training

DreamsPlus offers comprehensive Google Associate Cloud Engineer Certification Training to help you learn how to design, build, and manage cloud solutions. Our expert trainers provide hands-on practice, real-world examples, and personalized support to ensure your success.

Syllabus:

Section 1: Setting up a cloud solution environment (~20% of the exam)

1.1 Setting up cloud projects and accounts. Considerations include:

• Establishing a hierarchy of resources
• Implementing resource hierarchy policies inside the organization
• Assigning IAM roles to participants in a project
• Handling groups and users in Cloud Identity (both automatically and manually)
• Making APIs available for projects
• Provisioning and configuring items within the operations suite of Google Cloud
• Determining quotas and asking for raises

1.2 Managing billing configuration. Considerations include:

• Establishing one or more accounts for billing
• Connecting a project to an invoice
• Setting up notifications and billing budgets
•  Establishing exports for billing

Section 2: Planning and configuring a cloud solution (~17.5% of the exam)

2.1 Planning and configuring compute resources. Considerations include:

• • Choosing the right compute options (such as compute engine, Google Kubernetes engine, cloud run, and cloud functions) for a particular task

• When necessary, employ special machine types and Spot VM instances.

2.2 Planning and configuring data storage options. Considerations include:

• selecting a product (such as BigQuery, Firestore, Spanner, Cloud SQL, etc.)
• Selecting storage options (such as Standard, Nearline, Coldline, Archive, Standard, Regional, and zonal persistent disks)

2.3 Planning and configuring network resources. Considerations include:

• Balance of loads 
• The network’s resource locations’ accessibility
• Tiers of Network Services

Section 3: Deploying and implementing a cloud solution (~25% of the exam)

3.1 Deploying and implementing Compute Engine resources. Considerations include:

• Starting a computing instance, including setting up SSH keys, availability policies, and disks
•  Using an instance template to create an autoscaled managed instance group
• Setting Up OS Login
• Setting up VM Manager

3.2 Deploying and implementing Google Kubernetes Engine resources. Considerations include:

• Installing and setting up the Kubernetes command line interface (kubectl)
•  Setting up separate Google Kubernetes Engine cluster configurations (Autopilot, regional clusters, private clusters, GKE Enterprise, etc.)
•  Installing a Google Kubernetes Engine containerized program

  3.3 Deploying and implementing Cloud Run and Cloud Functions resources. Considerations include:

• Launching an application 
• Setting up an application to receive events from Google Cloud (such as Pub/Sub events, Cloud Storage object change notification events, and Eventarc, among others)
• Where to use Cloud Run (fully managed), Cloud Run for Anthos, or Cloud Functions to deploy an application

3.4 Deploying and implementing data solutions. Considerations include:

• Setting up data products (such as AlloyDB, Cloud SQL, Firestore, BigQuery, Spanner, Pub/Sub, Dataflow, and Cloud Storage)
•  Loading data (using Storage Transfer Service, Cloud Storage, command line upload, etc.)

3.5 Deploying and implementing networking resources. Considerations include:

•  Establishing a virtual private cloud (VPC) using subnets (such as a shared or custom mode VPC)
• Developing firewall rules and policies for entrance and egress (such as IP subnets, network tags, and service accounts).
•  Peering over external networks (such as VPC Network Peering and Cloud VPN)

3.6 Implementing resources through infrastructure as code. Considerations include:

•  Infrastructure as code tooling, such as Terraform, Helm, Config Connector, and Cloud Foundation Toolkit

Section 4: Ensuring successful operation of a cloud solution (~20% of the exam)

 4.1 Managing Compute Engine resources. Considerations include:

•  Connecting to the instance remotely
•  Seeing the inventory of operating virtual machines (e.g., instance IDs, information)
• Working with snapshots: creating, viewing, deleting, scheduling, and creating a snapshot from a virtual machine
•  Working with images: creating, viewing, deleting, and creating an image from a snapshot or virtual machine

4.2 Managing Google Kubernetes Engine resources. Considerations include:

•  Examining the cluster inventory that is currently in use (such as nodes, pods, and services)
• Setting up Google Kubernetes Engine for Artifact Registry Access
• Managing node pools, such as adding, modifying, or eliminating one
• Using Kubernetes resources (such as pods, services, and statefulsets) 
• Handling setups for both horizontal and vertical autoscaling

4.3 Managing Cloud Run resources. Considerations include:

• Updating application traffic splitting parameters 
• Implementing new application versions 
• Configuring scaling parameters for autoscaling instances

4.4 Managing storage and database solutions. Considerations include:

• Organizing and safeguarding items within cloud storage containers
•  Establishing policies for item lifecycle management in cloud storage buckets
•  Running queries on data instances (such as Cloud SQL, BigQuery, Spanner, Firestore, and AlloyDB) in order to obtain data
• Calculating the price of data storage resources
• Restoring and backing up database instances (such as Firestore and Cloud SQL).
• Examining the status of the job (such as Dataflow, BigQuery)

 4.5 Managing networking resources. Considerations include:

•  Adding a subnet to a VPC that already exists
• Adding more IP addresses to a subnet
• Setting aside static IP addresses, either internal or external
• Utilizing cloud DNS and cloud NAT services

4.6 Monitoring and logging. Considerations include:

•  Producing warnings for cloud monitoring by using resource metrics
•  Developing and consuming custom metrics for cloud monitoring (from logs or applications, for example)
• Exporting logs to third-party systems (like BigQuery and on-premises systems)
• Setting up log routers, log analytics, and log buckets
• Using cloud logging to see and filter logs
• Examining particular log message information in cloud logging
• Examining an application problem with cloud diagnostics
• Checking the status of Google Cloud
• Setting up and implementing Ops Agent
•  Implementing Prometheus Managed Service
• Setting up audit logs

Section 5: Configuring access and security (~17.5% of the exam)

5.1 Managing Identity and Access Management (IAM). Considerations include

•  Managing the different role types and defining custom IAM roles (such as basic, predefined, and custom)
• Seeing and setting IAM policies

5.2 Managing service accounts. Considerations include:

• Establishing service accounts
• using service accounts in IAM rules with restricted access
• designating service accounts to resources
• Overseeing a service account’s IAM
• Handling the impersonation of service accounts
• Establishing and maintaining temporary service account login credentials