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Prerequisites

Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.

The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.

Objective of Microsoft AI-900 Exam

It's important to note that the Microsoft AI-900 exam isn't an exam on Azure; the objective of the exam is to make sure you know how to build and manage intelligent cloud solutions and services on Azure. Foundation to Azure, or you can still get the certificate. Defined best practices for solution design, deployment, and administration. Identity of Azure services. Conclude testing by connecting the solution to the infrastructure to test for functionality. Instructor for the exam has extensive experience with Azure. Textidentify Azure services. Define identity of Azure services. Rewarding in the form of free exam vouchers and in-demand credential. Microsoft AI-900 exam dumps prepare you to become qualified Azure solutions specialist. Focus on the skills that are of high value in your organization. Bots are an important part of this exam.

Pay special attention to Cloud design principles. Terms used to describe the Azure platform. Standing out in the crowd and getting the job you want. Regular updates by Microsoft experts. Space for comments with every question. Sources for determining Azure pricing. Document your approach to meeting your goals. Positive impact of the exam on your career path.

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Exam Topics

The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:

Describe AI Considerations and Workloads (15-20%)

The first topic evaluates the following skills:

  • Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
  • Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of computer vision workloads on Azure15–20%- Identify types of computer vision solutions
- Describe capabilities of Azure Face
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Form Recognizer
Topic 2: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Identify types of NLP solutions
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Translator
- Describe capabilities of Azure Language
Topic 3: Artificial Intelligence workloads and considerations15–20%- Describe responsible AI principles
- Identify types of AI workloads
- Describe considerations for developing AI solutions
Topic 4: Features of generative AI workloads on Azure20–25%- Describe capabilities of Azure OpenAI Service
- Describe responsible AI practices for generative AI
- Describe generative AI concepts
- Describe use cases for generative AI
Topic 5: Fundamental principles of machine learning on Azure15–20%- Describe automated machine learning
- Describe capabilities of Azure Machine Learning
- Describe machine learning pipelines
- Describe core concepts of machine learning

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