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Microsoft AI-900 Questions - Tips To Pass Exam 2025

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The Microsoft AI-900 exam covers a range of topics including the fundamentals of AI and machine learning, the different types of AI models, and how to build and train machine learning models using Microsoft Azure. AI-900 exam is also designed to evaluate your understanding of key ethical and responsible AI principles and how to implement them in real-world scenarios.

The AI-900 certification exam is primarily designed for business stakeholders and technical professionals who want to gain a fundamental understanding of AI technologies and their potential applications in Microsoft Azure services. Microsoft Azure AI Fundamentals certification is an entry-level exam that validates the candidate's knowledge of AI concepts and Azure AI services, including Cognitive Services, Machine Learning, and Bot Services. AI-900 Exam also covers the ethical and responsible use of AI, including privacy, security, and compliance considerations.

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Microsoft Azure AI Fundamentals Sample Questions (Q188-Q193):

NEW QUESTION # 188
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0 All 3 are correct as they are the different channels to connect with a bot Office 365 email - Enable a bot to communicate with users via Office 365 email.
Microsoft Teams - Configure a bot to communicate with users through Microsoft Teams.
Web Chat - Automatically configured for you when you create a bot with the Bot Framework Service.
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0

NEW QUESTION # 189
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:
Explanation
Graphical user interface, text, application Description automatically generated

NEW QUESTION # 190
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?

A. regression
B. clustering
C. classification

Answer: A

Explanation:
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

NEW QUESTION # 191
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

NEW QUESTION # 192
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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaluate-model

NEW QUESTION # 193
......

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