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The Google Cloud Engineer Exam

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GCP offers 4 main services: - Compute (your virtual machine) - Storage - Big Data tools - Machine learning tools

ACE Exam v Professional Cloud Engineer

The Associate level exam (ACE) does not expect the applicant to be able to solve cloud business requirement problems. It focuses on technical requirements and implementation of cloud solutions (expect to be tested on syntax for CLI commands!).

It is the professional-level exam that requires the applicant to be able to critically examine various solutions and decide which best apply to a business requirement (in addition to the ACE materials).

ACE Exam Requirements

The ACE exam certifies that you are able to build, deploy, and manage the cloud services.

You will be expected to understand different ways of delivering cloud computing resources such as:

  • Virtual Machines (VMs)
  • Kubernetes

Computing resources may be allocated as individual VMs or clusters of VMs that you manage. Clusters may be managed by Kubernetes cluster (GKE) abstracting away much of the admin required in managing a Kubernetes cluster.

Kubernetes is just one of the serverless computing options offered by GC. GC is geared toward supporting microservices, that is code run in a containerized environment managed by the cloud provider or in a compute platform designed for short-running code.

Microservices may be managed by GC or Developers, and DevOps may manage their own servers and clusters. Managed services and serverless options are good choices when you do not need control over the computing environment and will get more value from abstracting such management away.

Google Cloud Engineers (GCEs) must understand the different forms of cloud storage and when to use them:

The professional-level GCE must understand the implications of replacing an IT environment on-premise with the cloud. Running an IT environment in the cloud has several advantages, including short-term rental of resources, a pay-as-you-go model, elastic resource allocation, and the choice of many specialised services. You can’t, however, assume that the unit cost of cloud resources will be cheaper in the cloud than on-premise. It is important to understand the cost models so you can provide advice about the most efficient distribution of workload between cloud and on-premise resources.

GCP Resources Overview

App Engine

App engine is typically used for websites, game backends, IOT, and - well, Apps.

Compute Engine

Compute engine provides access to VMs allowing users access to any OS, or configuration. It is also possible to create and apply custom VM images.

Kubenetes Engine

GKE is excellent for managing containerised workloads and hybrid applications.

Data Storage

Data Storage on the GCP has various modes:

  • object
  • file
  • block
  • in-memory caches

Object Storage

Object storage is designed for highly reliable and durable storage of objects such as images or datasets. Object storage is less versatile than file system–based storage systems which provide hierarchical directory storage for files and support operating system. File system services include providing accessibility to multiple servers.

Block Storage

Block storage occurs on persistent storage devices, such as SSDs and HDDs. Caches are temporary, in-memory data stores used to minimize the latency in retrieving data. They do not provide persistent storage and data held there is usually only recent, not current.

Database Storage

The GCP provides 6 different database options for data storage.

  • Big Query
  • Cloud Bigtable
  • Cloud Datastore / Firestore
  • Cloud Spanner
  • Cloud SQL
  • Cloud Storage