GCP
Course Overview
Google Cloud Platform is a suite of public cloud computing services offered by Google. The platform includes a range of hosted services for compute, storage and application development that run on Google hardware. Google Cloud Platform services can be accessed by software developers, cloud administrators and other enterprise IT professionals over the public internet or through a dedicated network connection.
Foundational certification
The foundational level certification validates broad knowledge of cloud concepts and Google Cloud products, services, tools, features, benefits, and use cases, specifically understanding the capabilities of Google Cloud.
This certification is appropriate for individuals in non-technical job roles who can add value in their organization by gaining Cloud knowledge and who have little or no hands-on experience in Google Cloud.
Associate certification
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud.
This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certification
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management.
These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
Foundational certification
A Cloud Digital Leader can distinguish and evaluate the various capabilities of Google Cloud core products and services and how they can be used to achieve desired business goals. A Cloud Digital Leader is well-versed in basic cloud concepts and can demonstrate a broad application of cloud computing knowledge in a variety of applications.
The Cloud Digital Leader exam is job-role independent. The exam assesses the knowledge and skills of individuals who want or are required to understand the purpose and application of Google Cloud products.
Associate certification
Associate Cloud Engineers deploy applications, monitor operations, and manage enterprise solutions. They use Google Cloud Console and the command-line interface to perform common platform-based tasks to maintain one or more deployed solutions that leverage Google-managed or self-managed services on Google Cloud.
Professional Cloud Architect
Professional Cloud Architects enable organizations to leverage Google Cloud technologies. With a thorough understanding of cloud architecture and Google Cloud, they design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.
Professional Cloud Developers build scalable and highly available applications using Google-recommended practices and tools. They have experience with cloud-native applications, developer tools, managed services, and next-generation databases. Cloud Developers are also proficient with at least one general-purpose programming language and are skilled at producing meaningful metrics and logs to debug and trace code.
Professional Data Engineers enable data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
A Professional Cloud DevOps Engineer is responsible for efficient development operations that can balance service reliability and delivery speed. They are skilled at using Google Cloud to build software delivery pipelines, deploy and monitor services, and manage and learn from incidents.
A Cloud Security Engineer enables organizations to design and implement secure workloads and infrastructure on Google Cloud. Through an understanding of security best practices and industry security requirements, this individual designs, develops, and manages a secure infrastructure by leveraging Google security technologies. The Cloud Security Engineer should be proficient in all aspects of cloud Security including identity and access management, defining organizational structure and policies, using Google technologies to provide data protection, configuring network security defenses, collecting and analyzing Google Cloud logs, managing incident responses, and demonstrating an understanding of the application of dynamic regulatory considerations.
A Professional Cloud Network Engineer implements and manages network architectures in Google Cloud. This individual may work on networking or cloud teams with architects who design cloud infrastructure. The Cloud Network Engineer uses the Google Cloud Console and/or command line interface, and leverages experience with network services, application and container networking, hybrid and multi-cloud connectivity, implementing VPCs, and security for established network architectures to ensure successful cloud implementations.
A Professional Collaboration Engineer transforms business objectives into tangible configurations, policies, and security practices as they relate to users, content, and integrations. Through their understanding of their organization’s infrastructure, Collaboration Engineers enable people to work together, communicate, and access data in a secure and efficient manner. Operating with an engineering and solutions mindset, they use tools, programming languages, and APIs to automate workflows. They look for opportunities to educate end users and increase operational efficiency while advocating for Google Workspace and the Google toolset.
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
