Microsoft AI-900 Fundamental: A look at the Official Study Guide For Exam

AI-900 Exam

Now that Artificial Intelligence is the thing of the present, it has become essential for people in the software development industry to get a hold of the AI terms, tools, and technologies.

If you are a student who wants to pursue a career in AI & ML, below are some roles in AI that are in high demand.

AI Industry Roles & Specializations:

    Role Job Description
    AI Product Manager To oversee the development and deployment of AI-powered products or solutions.
    Machine Learning Engineer Designing, implementing, and maintaining machine learning systems and algorithms.
    Data Engineer Build large data pipelines, design and optimize databases, and ensure data quality and integrity.
    AI Developer Develop software systems and applications that incorporate AI capabilities.
    Data Scientist Analyze complex datasets to extract insights and patterns by using statistical techniques and machine learning algorithms.
    AI Research Scientist Work on advancing the field of AI by conducting R&D on new algorithms, techniques, and models.
    Natural Language Processing (NLP) Engineer Develop algorithms and models that understand and process human language.
    Computer Vision Engineer Develop algorithms and systems that can interpret and understand visual information.
    AI Ethicist Address the ethical implications and societal impact of AI systems.
    AI Consultant Provide strategic guidance to businesses on leveraging AI technologies.

One of the best way to know the fundamentals of AI & ML is by getting a certification from AI-900: Microsoft Azure AI Fundamentals.

About AI-900: Microsoft Azure AI Fundamentals exam

    The AI-900 is the entry-level exam that does not require you to be a technical person, if you come from a non-technical background you should be able to grasp the concepts of this course.

    Let's take a look at the official study guide for this exam:

    Topic Weightage
    Describe Artificial Intelligence workloads and considerations 20–25%
    Describe fundamental principles of machine learning on Azure 25–30%
    Describe features of computer vision workloads on Azure 15–20%
    Describe features of Natural Language Processing (NLP) workloads on Azure 25–30%

Links to Important Official Microsoft Study Guide & Resources

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