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AI-900 Exam Questions

Exam Code:
AI-900
Exam Name:
Microsoft Azure AI Fundamentals
Last Update: Jan 20, 2022
138 Questions
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AI-900 Exam Questions

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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Microsoft Azure AI Fundamentals Questions and Answers

Questions 1

You build a machine learning model by using the automated machine learning user interface (UI).

You need to ensure that the model meets the Microsoft transparency principle for responsible AI.

What should you do?

Options:

A.

Set Validation type to Auto.

B.

Enable Explain best model.

C.

Set Primary metric to accuracy.

D.

Set Max concurrent iterations to 0.

Questions 2

Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.

This an example of which Microsoft guiding principle for responsible AI?

Options:

A.

accountability

B.

fairness

C.

inclusiveness

D.

privacy and security

Questions 3

For a machine learning progress, how should you split data for training and evaluation?

Options:

A.

Use features for training and labels for evaluation.

B.

Randomly split the data into rows for training and rows for evaluation.

C.

Use labels for training and features for evaluation.

D.

Randomly split the data into columns for training and columns for evaluation.

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