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Microsoft AI-900 Dumps

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Microsoft Azure AI Fundamentals
Last Update: Feb 21, 2024
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Microsoft Azure AI Fundamentals

The Microsoft Azure AI Fundamentals is an exam that tests a candidate's knowledge regarding the fundamental and foundational skills of Microsoft. These skills are as simple as the fundamentals of Machine Learning (ML), Artificial Intelligence (AI), and the concepts and skills related to the Microsoft Azure Services. The candidates who can pass the Microsoft Azure AI Fundamentals will earn the Microsoft Azure AI Fundamentals certification. By acquiring this certification, the candidate gets a chance to demonstrate their knowledge of essential ML and AI workloads. They will also be able to showcase the implementation of these workloads on Azure. The Microsoft Azure AI Fundamentals certification is meant for candidates from both technical and non-technical backgrounds. No prior data science or software engineering experience is needed, but some basic programming knowledge would help.

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The AI-900 certification, also known as the Microsoft Azure AI Fundamentals certification, validates foundational knowledge of artificial intelligence concepts and Microsoft Azure AI services. It's a great entry point for those looking to understand AI's basics and its application within the Azure cloud ecosystem.

The AI-900 exam typically consists of 45 questions. This certification assessment evaluates candidates' fundamental knowledge of artificial intelligence concepts and their ability to apply these principles within the Microsoft Azure environment.

A practical use case for classification in the AI-900 context is sentiment analysis for customer reviews. By categorizing feedback as positive, negative, or neutral, businesses can gauge customer satisfaction and make data-driven improvements to their products and services.

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Microsoft Azure AI Fundamentals Questions and Answers

Questions 1

You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.



Education Level


Last Name




Income Range


First Name

Questions 2

When training a model, why should you randomly split the rows into separate subsets?



to train the model twice to attain better accuracy


to train multiple models simultaneously to attain better performance


to test the model by using data that was not used to train the model

Questions 3

Match the types of machine learning to the appropriate scenarios.

To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.