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Describe NLP Workloads Features on Azure (15-20%)

This domain contains the following details that you need to learn about:

  • Identify Azure services & tools for Natural Language Processing Workloads – This topic is created to equip you with the ability to identify various capabilities, such as Speech service, Text Analytics service, Translator Text service, and Language Understanding service.
  • Identify the features of basic NLP (Natural Language Processing) Workload Scenarios – The individuals should be able to identify various uses and features of various components, for example, keyphrase extraction, sentiment analysis, entity recognition, translation, language modeling, and speech recognition & synthesis.

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

Preparation Process

Microsoft offers a wide range of resources to help you prepare for your certification exam. The first step in the preparation process is to go through the topics in detail. This will enable you to understand the specific areas that are covered in the test. With this understanding, you can start to get the materials that will provide more knowledge on the objectives. Thus, Microsoft comes with two training options to help the candidates develop the required skills. These are free online training and instructor-led training.

  • Instructor-Led Training: The Microsoft Azure AI Fundamentals course is designed to help you gain the required skills to pass the exam and obtain the certification. It introduces the learners to the fundamental concepts of AI and the services available in Microsoft Azure that are useful in creating AI solutions. This training is delivered by the certified partners and its details can be found on the Microsoft website. It is important to mention that this course is designed to be combined with other online materials that are available on the Microsoft Learning platform.
  • Practice Test: As a future examinee, you also have access to the official practice test to evaluate your understanding of the exam topics before taking the actual test. The link to this training tool can be found on the Microsoft website.
  • Free Online Training: The individuals can explore five free training tutorials that are available on the official exam webpage. They have a series of modules that focus on the specific domains of the test. These training tutorials are recommended for those candidates who are comfortable with the self-paced study. The topics of these manuals include the following options:
    • Get Started with AI on Azure;
    • Create No-Code Predictive Models with Azure ML;
    • Explore NLP;
    • Explore Computer Vision on Microsoft Azure;
    • Explore Conversational Artificial Intelligence.

    The students can find the details of these learning paths on the official website.

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Microsoft AI-900 中文 Exam Syllabus Topics:

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

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