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Microsoft AI-900 exam questions in PDF file
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Microsoft Azure Certifications
AI-102 Exam: Designing and Implementing a Microsoft Azure AI Solution (beta)
Free AI-102 Exam Questions Answers
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Following are some Microsoft AI-900 exam questions for review (Microsoft AI-900 practice test 1-13)
QUESTION 1
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. knowledgeability
B. decisiveness
C. inclusiveness
D. fairness
E. opinionatedness
F. reliability and safety
Correct Answer: CDF
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
QUESTION 2
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
QUESTION 3
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. a telephone answering service that has a pre-recorder message
B. a chatbot that provides users with the ability to find answers on a website by themselves
C. telephone voice menus to reduce the load on human resources
D. a service that creates frequently asked questions (FAQ) documents by crawling public websites
Correct Answer: BC
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to
communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all
businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward
algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web
content.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
QUESTION 4
DRAG DROP
Match the types of computer vision 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.
Box 1: Facial recognition Face detection that perceives faces and attributes in an image; person identification that
matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a
range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces
in images.
Box 2: OCR
Box 3: Objection detection Object detection is similar to tagging, but the API returns the bounding box coordinates (in
pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list
those objects together with their coordinates in the image. You can use this functionality to process the relationships
between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an
image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal
relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API
only finds objects and living things, while the Tag API can also include contextual terms like “indoor”, which can\\’t be
localized with bounding boxes.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
QUESTION 5
DRAG DROP
Match the types of natural languages processing 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.
Select and Place:
Correct Answer:
Box 1: Key phrase extraction
Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people,
places, and organizations.
Box 2: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 3: Translation
Using Microsoft\\’s Translator text API
This versatile API from Microsoft can be used for the following:
Translate text from one language to another.
Transliterate text from one script to another.
Detecting language of the input text.
Find alternate translations to specific text.
Determine the sentence length.
Incorrect Answers:
Not Natural language processing (NLP), which is used for tasks such as sentiment analysis, topic detection, language
detection, key phrase extraction, and document categorization.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
QUESTION 6
DRAG DROP
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be
used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named
entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time,
quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information
on the
web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
QUESTION 7
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Reference: https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
QUESTION 8
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Correct Answer:
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection,
key phrase extraction, and document categorization.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-languageprocessing
QUESTION 9
DRAG DROP
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used
once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer:
Box 1: verification Face verification: Check the likelihood that two faces belong to the same person and receive a
confidence score. Box 2: similarity
Box 3: Grouping Box 4: identification Face detection: Detect one or more human faces along with attributes such as:
age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
QUESTION 10
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability
Correct Answer: B
QUESTION 11
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. a smart device in the home that responds to questions such as “What will the weather be like today?”
B. a website that uses a knowledge base to interactively respond to users\\’ questions
C. assembly line machinery that autonomously inserts headlamps into cars
D. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold
Correct Answer: AB
QUESTION 12
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?
A. semantic segmentation
B. image classification
C. object detection
D. optical character recognition (OCR)
Correct Answer: D
Azure\\’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or
handwritten text from images. You can extract text from images, such as photos of license plates or containers with
serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text
QUESTION 13
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: Yes
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes
Azure bot service engages with customers in a conversational manner.
Box 3: No
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot.
When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
PS.
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