[Nov 04, 2022] AI-900 Exam Brain Dumps - Study Notes and Theory [Q70-Q93]

Topic	Details
Describe Artificial Intelligence workloads and considerations (20-25%)
Identify features of common AI workloads	– identify features of anomaly detection workloads – identify computer vision workloads – identify natural language processing workloads – identify knowledge mining workloads
Identify guiding principles for responsible AI	– describe considerations for fairness in an AI solution – describe considerations for reliability and safety in an AI solution – describe considerations for privacy and security in an AI solution – describe considerations for inclusiveness in an AI solution – describe considerations for transparency in an AI solution – describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (25-30%)
Identify common machine learning types	– identify regression machine learning scenarios – identify classification machine learning scenarios – identify clustering machine learning scenarios
Describe core machine learning concepts	– identify features and labels in a dataset for machine learning – describe how training and validation datasets are used in machine learning
Describe capabilities of visual tools in Azure Machine Learning studio	– automated machine learning – azure Machine Learning designer
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution	– identify features of image classification solutions – identify features of object detection solutions – identify features of optical character recognition solutions – identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks	– identify capabilities of the Computer Vision service – identify capabilities of the Custom Vision service – identify capabilities of the Face service – identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)
Identify features of common NLP Workload Scenarios	– identify features and uses for key phrase extraction – identify features and uses for entity recognition – identify features and uses for sentiment analysis – identify features and uses for language modeling – identify features and uses for speech recognition and synthesis – identify features and uses for translation

Q70. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Q71. You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
* Accept customer orders.
* Retrieve support documents.
* Retrieve order status updates.
Which type of NLP should you use?

sentiment analysis

translation

language modeling

named entity recognition

Q72. To complete the sentence, select the appropriate option in the answer area.

Q73. You plan to develop a bot that will enable users to query a knowledge base by using natural language processing.
Which two services should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

QnA Maker

Azure Bot Service

Form Recognizer

Anomaly Detector

Q74. To complete the sentence, select the appropriate option in the answer area.

Q75. Select the answer that correctly completes the sentence.

Q76. To complete the sentence, select the appropriate option in the answer area.

Q77. Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Q78. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Q79. You have the process shown in the following exhibit.

Which type AI solution is shown in the diagram?

a sentiment analysis solution

a chatbot

a machine learning model

a computer vision application

Q80. For a machine learning progress, how should you split data for training and evaluation?

Use features for training and labels for evaluation.

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

Use labels for training and features for evaluation.

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

Q81. In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

an in-car system that reads text messages aloud

providing closed captions for recorded or live videos

creating an automated public address system for a train station

creating a transcript of a telephone call or meeting

Q82. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Q83. You need to make the press releases of your company available in a range of languages.
Which service should you use?

Translator Text

Text Analytics

Speech

Language Understanding (LUIS)

Q84. You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?

language detection

sentiment analysis

key phrase extraction

entity recognition

Q85. You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?

inclusiveness

reliability and safety

accountability

fairness

Q86. You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?

semantic segmentation

image classification

object detection

optical character recognition (OCR)

Q87. Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Q88. You need to determine the location of cars in an image so that you can estimate the distance between the cars.
Which type of computer vision should you use?

optical character recognition (OCR)

object detection

image classification

face detection

Q89. To complete the sentence, select the appropriate option in the answer area.

Q90. You need to develop a chatbot for a website. The chatbot must answer users’ questions based on the information in the following documents:
A product troubleshooting guide in a Microsoft Word document
A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?

Azure Bot Service

Language Understanding

Text Analytics

QnA Maker

Q91. Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Q92. 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?

accountability

fairness

inclusiveness

privacy and security

Q93. You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
For each of the following statements select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

[Nov 04, 2022] AI-900 Exam Brain Dumps – Study Notes and Theory [Q70-Q93]

Requisites of Microsoft AI-900 Exam

Microsoft AI-900 Exam Syllabus Topics:

Describe Artificial Intelligence workloads and considerations (20-25%)

Describe fundamental principles of machine learning on Azure (25-30%)

Describe features of computer vision workloads on Azure (15-20%)

Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)

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