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AI-100 Exam Has Been Retired

This exam has been replaced by Microsoft with new exam.

Microsoft AI-100 Exam Details

The Microsoft AI-100 exam is a way to prove yourself skillful in the matters of AI. It is a prerequisite for the Microsoft Certified: Azure AI Engineer Associate certification meant for individuals with intermediate skills. The resultant job role for candidates is the Azure AI Engineer.

About the Microsoft AI-100 Exam

This test related to how to develop and execute solutions for Azure AI is intended for specialists with clear capabilities in aspects such as machine learning, cognitive services, and implementing solutions for Microsoft AI. Also, candidates for AI-100 are skilled in processing natural language, conversational AI, computer vision, and speech.

Candidates organizing themselves for this AI-100 test should have familiarity with developing and executing AI apps alongside agents utilizing Cognitive Services, Data Storage, Bot Service, and Cognitive Search for Azure. Additionally, they should be in a position to suggest solutions that utilize technologies for open source, comprehend the elements making up the AI portfolio for Azure, and the options available for data storage. They should as well be aware of when to develop a standard API that meets specific requirements.

AI-100 Exam Details

Exam-takers will face a test containing from 40 to 60 questions of different types. Some will be multiple choices, build lists, case studies, active screen, and short answers, while there are also other items like best answer, review screen, etc. Completing this test should occur within 3 hours and it is expected of you to reach 700 points and more to get a pass. The registration process for AI-100 demands $165.

Exam Topics

Different topics will appear for your AI-100 exam and candidates should be keen to understand them. Here are the domains with their details described:

  • Analyzing Solution Requirements

    Candidates start this objective by covering Cognitive Services APIs for Azure that satisfy requirements for a business. This includes selecting the processing infrastructure targeting a solution, selecting the proper technologies for data processing, choosing the proper models as well as services for AI, identifying the elements & technologies needed for connecting service endpoints, and identifying automation requirements. The next section follows by mapping security demands to technologies, tools, and processes. Included here are processes and controls required for conforming to data protection, privacy and requirements for regulations, users alongside groups to access information as well as interfaces, proper tools for solutions, and auditing requirements. This topic is succeeded by the domain for selecting the services, storage, and software needed for supporting solutions. Matters to cover within this part include proper tools and services targeted at a solution, points of integration with other services for Microsoft, and storage needed for logging, Azure-based Cognitive Services, and bot state data output.

  • Designing AI solutions

    This domain for AI-100 exam includes five subsections. The first one is for designing solutions that have at least one pipeline. Matters addressed in this subtopic are workflow processes for AI applications, strategies for ingesting and egressing data, points of integration between the pipeline and multifold workflows, and pipelines that utilize AI apps. Addressed as well are matters to do with pipelines responsible for calling Machine Learning models for Azure and an AI solution that satisfies cost constraints. The second subsection concerns developing solutions that utilize Cognitive Services. It involves developing such solutions that utilize speech, language, vision, search, knowledge, and anomaly APIs for detection. The third segment stresses the aspect of developing solutions that execute the framework of Microsoft Bot. To perform are tasks like integrating bots alongside AI solutions, developing bot services using Language Understanding, developing bots capable of integrating with channels, and integrating bots with app services as well as Application Insights for Azure. The fourth subsection regards developing compute architecture for supporting a solution. This concerns the possibility of creating a GPU, CPU, or FPGA-based solution, using an on-premise, cloud-based, or hybrid compute architecture and selecting compute solutions that satisfy cost constraints. The fifth and final subtopic concerns development for data compliance, governance, security, and integrity. It includes users and app authentication to services of AI, developing a strategy for content moderation targeting data utilization within a given AI solution, ensuring that data adheres to requirements of compliance as defined by your company, and ensuring proper data governance. Included also is the developing techniques that ensure that every solution satisfies regulations for data privacy and standards for the industry.

  • Implementing and monitoring Solutions for AI

    This final part concerns a number of things. To begin is executing a workflow related to AI. This is where you will experience tasks including developing AI pipelines, managing data flow through elements of the solution, executing processes for data logging, and defining & constructing interfaces targeting standard AI services. You will also come across the items related to creating solution endpoints and developing streaming solutions. The next thing addresses the integration of services for AI and solution elements. The tasks within this domain include configuring prerequisite elements as well as input datasets so that they can allow Cognitive Service APIs consumption, configuring integration with Cognitive Services, and prerequisite elements so that it could enable connectivity to the Bot Framework for Microsoft. Also included here is the part of executing Cognitive Search in solutions. The last bit of this AI-100 objective is on monitoring and evaluating the environment for AI. Tasks to address deal with learning how different KPIs are from reported metrics and what causes their differences. Additionally, you must differentiate between the actual & expected workflow throughputs, maintain the AI solution targeted at continuous improvement, and monitor AI elements for availability.

Responsibilities and Salary of Azure AI Engineer

A qualified Azure AI engineer who has passed AI-100 exam and received the related certification will be responsible for analyzing what the AI solutions require, recommending the proper technologies & tools, and developing as well as executing AI solutions that satisfy performance and scalability requirements. The estimation gives $165k as the expected pay for AI engineers.

What’s Next?

The most natural thing to do after the Microsoft Certified: Azure AI Engineer Associate is to earn the Microsoft Certified: Azure Solutions Architect Expert certification. With this, you will expose yourself to the most demanded skills in the arena of Azure. It will result in better options for you as far as your career is concerned.