Microsoft Azure AI Fundamentals Practice Test

AI-900 Exam Format | Course Contents | Course Outline | Exam Syllabus | Exam Objectives

EXAM NUMBER : AI-900
EXAM NAME : Microsoft Azure AI Fundamentals
Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required;
however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Describe AI workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of conversational AI workloads on Azure

Module 1: Introduction to AI
In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development.

Artificial Intelligence in Azure
Responsible AI
After completing this module you will be able to:

Describe Artificial Intelligence workloads and considerations
Module 2: Machine Learning
Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.

Introduction to Machine Learning
Azure Machine Learning
After completing this module you will be able to:

Describe fundamental principles of machine learning on Azure
Module 3: Computer Vision
Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services.

Computer Vision Concepts
Computer Vision in Azure
After completing this module you will be able to:

Describe features of computer vision workloads on Azure
Module 4: Natural Language Processing
This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.

After completing this module you will be able to:

Describe features of Natural Language Processing (NLP) workloads on Azure
Module 5: Conversational AI
Conversational AI enables users to engage in a dialog with an AI agent, or bot, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.

Conversational AI Concepts
Conversational AI in Azure
After completing this module you will be able to:

Describe features of conversational AI workloads on Azure

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Question #66
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
Include one or more faces.
Contain at least one person wearing sunglasses.
What should you use to analyze the images?
A. the Verify operation in the Face service
B. the Detect operation in the Face service
C. the Describe Image operation in the Computer Vision service
D. the Analyze Image operation in the Computer Vision service
Answer: B
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
Question #67
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
A. clustering
B. regression
C. classification
Answer: C
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction
Question #68
When training a model, why should you randomly split the rows into separate subsets?
A. to train the model twice to attain better accuracy
B. to train multiple models simultaneously to attain better performance
C. to test the model by using data that was not used to train the model
Answer: C
Question #69
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Use a graphical user interface (GUI) to run automated machine learning experiments.
B. Create a compute instance to use as a workstation.
C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
D. Create a dataset from a comma-separated value (CSV) file.
Answer: AC
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
Question #70
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Extract the invoice number from an invoice.
B. Translate a form from French to English.
C. Find image of product in a catalog.
D. Identity the retailer from a receipt.
Answer: AD
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
Question #71
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. an in-car system that reads text messages aloud
B. providing closed captions for recorded or live videos
C. creating an automated public address system for a train station
D. creating a transcript of a telephone call or meeting
Answer: BD
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
Question #72
You need to build an app that will read recipe instructions aloud to support users who have reduced vision.
Which version service should you use?
A. Text Analytics
B. Translator Text
C. Speech
D. Language Understanding (LUIS)
Answer: C
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features
Question #73
Which scenario is an example of a webchat bot?
A. Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews.
B. Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.
C. Accept questions through email, and then route the email messages to the correct person based on the content of the message.
D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
Answer: D
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