2025/November Latest Braindump2go AI-900 Exam Dumps with PDF and VCE Free Updated Today! Following are some new Braindump2go AI-900 Real Exam Questions!
QUESTION 104
You use Azure Machine Learning designer to build a model pipeline. What should you create before you can run the pipeline?
A. a Jupyter notebook
B. a registered model
C. a compute resource
Answer: C
QUESTION 105
You need to build an image tagging solution for social media that tags images of your friends automatically.
Which Azure Cognitive Services service should you use?
A. Computer Vision
B. Face
C. Text Analytics
D. Form Recognizer
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/howtodetectfacesinimage
QUESTION 106
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds.
This is an example of which type of computer vision?
A. scene segmentation
B. optical character recognition (OCR)
C. object detection
Answer: C
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. 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.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview
QUESTION 107
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Python
B. R
C. C#
D. Scala
Answer: AB
Explanation:
Use Azure Machine Learning designer for customizing using Python and R code.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/designer/#features
QUESTION 108
Hotspot Question
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:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
QUESTION 109
Hotspot Question
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:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
QUESTION 110
Hotspot Question
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/architecture/reference-architectures/ai/conversational-bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0
QUESTION 111
Hotspot Question
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:
The translator service provides multi-language support for text translation, transliteration, language detection, and dictionaries.
Speech-to-Text, also known as automatic speech recognition (ASR), is a feature of Speech Services that provides transcription.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overview
https://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note
QUESTION 112
Hotspot Question
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:
Box 1: Yes
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Box 2: No
A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.
Box 3: No
Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
QUESTION 113
Hotspot Question
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:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/5-create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction
https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/1-introduction
QUESTION 114
Hotspot Question
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:
QUESTION 115
Drag and Drop Question
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point
Answer:
QUESTION 116
You are working for a car dealership. Your boss asks you to provide him with forecast information. Will the new car model be successful or not? The new model has a variety of engine improvements, more comfortable seats, and a sunroof. You compiled the list of data about previous successful models with their characteristics and sales numbers.
What should you do in the pre-processing data stage that would help you predict the new model’s success?
A. Data selection
B. Training set selection
C. Data for model evaluation selection
D. Feature selection
E. Data classification
Answer: D
Explanation:
During pre-processing, you need to work with data to select features that influence the label prediction. In this problem, features are the engine characteristics (power or volume), seat comforts, etc. They could help the ML model to predict the success of the new car model. Maybe the sunroof is not essential for predicting the label, and we need to discard this feature from the final set of features that we will use for model training.
In short, Feature selection helps us to narrow down the features that are important for our label prediction and discard all features that don’t play or play a minimal role in a label prediction. As a result, our trained model and prediction will be more efficient.
All other options are incorrect because they are parts of the different data processing events that are irrelevant to the pre-processing (Training set selection or Data for model evaluation selection) or too generic (Data selection or Data Classification).
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/select-features.
QUESTION 117
Hotspot Question
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:
QUESTION 118
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.
A. QnA Maker
B. Azure Bot Service
C. Form Recognizer
D. Anomaly Detector
Answer: AB
Explanation:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
QUESTION 119
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
B. generating live captions for a news broadcast
C. extracting key phrases from the audio recording of a meeting
D. an AI character in a computer game that speaks audibly to a player
Answer: AD
Explanation:
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Incorrect Answers:
C: Extracting key phrases is not speech synthesis.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
QUESTION 120
Drag and Drop Question
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
Which companies were mentioned in a news article?
Does a tweet contain the name of a person? Does the tweet also provide his current location?
Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API’s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral” and “positive”) and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis
QUESTION 121
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Generate the questions and answers from an existing webpage.
B. Use automated machine learning to train a model based on a file that contains the questions.
C. Manually enter the questions and answers.
D. Connect the bot to the Cortana channel and ask questions by using Cortana.
E. Import chit-chat content from a predefined data source.
Answer: ACE
Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
QUESTION 122
You have a frequently asked questions (FAQ) PDF file.
You need to create a conversational support system based on the FAQ.
Which service should you use?
A. QnA Maker
B. Text Analytics
C. Computer Vision
D. Language Understanding (LUIS)
Answer: A
Explanation:
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
QUESTION 123
You need to reduce the load on telephone operators by implementing a chatbot to answer simple questions with predefined answers.
Which two AI service should you use to achieve the goal? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Text Analytics
B. QnA Maker
C. Azure Bot Service
D. Translator Text
Answer: BC
Explanation:
Bots are a popular way to provide support through multiple communication channels. You can use the QnA Maker service and Azure Bot Service to create a bot that answers user questions.
Reference:
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/
QUESTION 124
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
Answer: AB
Explanation:
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-language-processing
QUESTION 125
What is the main foundation for the Personal Digital Assistant?
A. Azure Speech
B. Bot Framework
C. Computer Vision Service
D. Text Analytics
E. Automated Machine Learning
Answer: B
QUESTION 126
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you use?
A. Custom Vision
B. QnA Maker
C. Translator Text
D. Face
Answer: B
Explanation:
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Incorrect Answers:
A: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
QUESTION 127
Which AI service should you use to create a bot from a frequently asked questions (FAQ) document?
A. QnA Maker
B. Language Understanding (LUIS
C. Text Analytics
D. Speech
Answer: A
QUESTION 128
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
QUESTION 129
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
Answer: BC
Explanation:
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
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