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Microsoft AI-900 exam measures the candidates' knowledge of essential AI concepts, including data analysis, machine learning, and natural language processing. AI-900 exam also covers the Azure services that support AI, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. Passing AI-900 Exam demonstrates that the candidate has a foundational understanding of AI and its applications in Azure and is capable of working with these technologies.

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The AI-900 exam is an entry-level certification that does not require any prerequisites. However, it is recommended that you have some basic knowledge of cloud computing and programming concepts before taking AI-900 exam. AI-900 exam is available in multiple languages and can be taken online or at a testing center.

Microsoft AI-900 (Microsoft Azure AI Fundamentals) Exam is a certification exam that focuses on the basics of Artificial Intelligence (AI) and its applications in Azure. AI-900 Exam is designed to help professionals and students understand the core principles of AI, including machine learning, natural language processing, computer vision, and cognitive services. AI-900 exam also covers the fundamentals of Azure AI services, including Azure Machine Learning, Azure Cognitive Services, and Azure Bot Service.

Microsoft Azure AI Fundamentals Sample Questions (Q280-Q285):

NEW QUESTION # 280
What is a use case for classification?

A. analyzing the contents of images and grouping images that have similar colors
B. predicting how many minutes it will take someone to run a race based on past race times
C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
D. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.

Answer: C

NEW QUESTION # 281
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: 11

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

NEW QUESTION # 282
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point

Answer:

Explanation:

NEW QUESTION # 283
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?

A. clustering
B. regression
C. classification

Answer: B

Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction

NEW QUESTION # 284
To complete the sentence, select the appropriate option in the answer area.