Cloud Digital Leader

A Cloud Digital Leader can articulate the capabilities of Google Cloud core products and services and how they benefit organizations. The Cloud Digital Leader can also describe common business use cases and how cloud solutions support an enterprise. This certification is for anyone who wishes to demonstrate their knowledge of cloud computing basics and how Google Cloud products and services can be used to achieve an organization’s goals.

The Cloud Digital Leader exam assesses your knowledge in these areas:

Cloud Digital Leader Form

Associate Cloud Engineer

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The Associate Cloud Engineer exam assesses your ability to:

Associate Cloud Engineer Form

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.

The Professional Cloud Architect certification exam assesses your ability to:

Professional Cloud Architect Form

Professional Cloud Developer

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended tools and best practices. This individual has experience with cloud-native applications, developer tools, managed services, and nextgeneration databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and instruments their code to produce metrics, logs, and traces.

The Professional Cloud Developer exam assesses your ability to:

Professional Cloud Developer Form

Professional Data Engineer

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.

The Professional Data Engineer exam assesses your ability to:

Professional Data Engineer Form

Professional Cloud DevOps Engineer

Professional Cloud DevOps Engineers implement processes throughout the systems development lifecycle using Google-recommended methodologies and tools. They build and deploy software and infrastructure delivery pipelines, optimize and maintain production systems and services, and balance service reliability with delivery speed.

The Professional Cloud DevOps Engineer exam assesses your ability to:

Professional Cloud DevOps Engineer Form

Professional Cloud Security Engineer

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 person designs, develops, and manages a secure solution by using Google security technologies. A Cloud Security Engineer should be proficient in all aspects of cloud security. This includes identity and access management, defining organizational structure and policies, using Google Cloud technologies to provide data protection, configuring network security defenses, monitoring environments for threat detection and incident response, security policy as code, the secure software development lifecycle, and enforcing regulatory controls.

The Professional Cloud Security Engineer exam assesses your ability to:

Professional Cloud Security Engineer Form

Professional Google Workspace Administrator

A Professional Google Workspace Administrator transforms business objectives into tangible Google Workspace configurations, policies, and security practices as they relate to users, content, and integrations. Through their understanding of their organization's infrastructure, Google Workspace Administrators let people work together, communicate, and access data in a secure and efficient manner. Operating with an engineering and solutions mindset, Google Workspace Administrators use tools, programming languages, and APIs to automate workflows, educate end users, and increase operational efficiency while they advocate for Google Workspace and its toolset.

Related job roles: IT systems administrator, cloud solutions engineer, collaboration engineer, systems engineer

The Professional Google Workspace Administrator exam assesses your ability to:

Profetional Google Workspace Administrator Form

Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

Candidates for this exam should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Responsibilities for this role include designing and creating a suitable working environment for data science workloads; exploring data; training machine learning models; implementing pipelines; running jobs to prepare for production; and managing, deploying, and monitoring scalable machine learning solutions. A candidate for this exam should have knowledge and experience in data science by using Azure Machine Learning and MLflow

Skills you'll learn

Exam DP-100: Designing and Implementing a Data Science Solution on Azure Form

Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions

DevOps engineers are developers or infrastructure administrators who also have subject matter expertise in working with people, processes, and products to enable continuous delivery of value in organizations. Responsibilities for this role include designing and implementing strategies for collaboration, code, infrastructure, source control, security, compliance, continuous integration, testing, delivery, monitoring, and feedback. DevOps engineers work on cross-functional teams that include developers, site reliability engineers, and Azure administrators. DevOps engineers must have experience with administering and developing in Azure, with strong skills in at least one of these areas. They should be familiar with both Azure DevOps and GitHub.

Skills you'll learn

Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions Form

Microsoft Certified: DevOps Engineer Expert

DevOps engineers are developers or infrastructure administrators who also have subject matter expertise in working with people, processes, and products to enable continuous delivery of value in organizations. Responsibilities for this role include designing and implementing strategies for collaboration, code, infrastructure, source control, security, compliance, continuous integration, testing, delivery, monitoring, and feedback. DevOps engineers work on cross-functional teams that include developers, site reliability engineers, and Azure administrators.

Skills you'll learn

Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions Form

Microsoft Certified: Azure Data Scientist Associate

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Responsibilities for this role include designing and creating a suitable working environment for data science workloads; exploring data; training machine learning models; implementing pipelines; running jobs to prepare for production; and managing, deploying, and monitoring scalable machine learning solutions.

Skills you'll learn

Microsoft Certified: Azure Data Scientist Associate Form

Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of Azure Cognitive Services and Azure services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring. These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions. Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.

Skills you'll learn

Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution Form

Exam AI-900: Microsoft Azure AI Fundamentals

This exam is an opportunity to demonstrate knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. Candidates for this exam should have familiarity with Exam AI-900’s self-paced or instructor-led learning material. This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server applications would be beneficial. Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.

Skills you'll learn

Exam AI-900: Microsoft Azure AI Fundamentals Form

Microsoft Certified: Microsoft Certified: Azure AI Engineer Associate

Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of Azure Cognitive Services and Azure services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring. These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions. Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.

Skills you'll learn

Microsoft Certified: Azure AI Engineer Associate Form

Exam DP-203: Data Engineering on Microsoft Azure

Candidates for this exam should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions. Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including modern data warehouse (MDW), big data, or lakehouse architecture. Azure data engineers also help to ensure that the operationalization of data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. These professionals help to identify and troubleshoot operational and data quality issues. They also design, implement, monitor, and optimize data platforms to meet the data pipelines. Candidates for this exam must have solid knowledge of data processing languages, including SQL, Python, and Scala, and they need to understand parallel processing and data architecture patterns. They should be proficient in using Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage, and Azure Databricks to create data processing solutions.

Skills you'll learn

Exam DP-203: Data Engineering on Microsoft Azure Form