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NEW QUESTION 111
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
NEW QUESTION 112
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
C. Text Analytics
D. Form Recognizer
NEW QUESTION 113
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
NEW QUESTION 114
You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base?
NEW QUESTION 115
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 m a Microsoft Word document.
– A frequently asked questions (FAQ) list on a webpage.
Which service should you use to process the documents?
A. Language Undemanding
B. Text Analytics
C. Azure Bot Service
D. QnA Maker
NEW QUESTION 116
You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do?
A. Provision an inference duster.
B. Explain the model.
C. Register the model.
D. Register the training data.
NEW QUESTION 117
You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of Al workload should you use?
A. conversational Al
B. anomaly detection
C. natural language processing
D. computer vision
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
NEW QUESTION 118
In which scenario should you use key phrase extraction?
A. translating a set of documents from English to German
B. generating captions for a video based on the audio track
C. identifying whether reviews of a restaurant are positive or negative
D. identifying which documents provide information about the same topics
NEW QUESTION 119
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat. Which service should you use?
A. Text Analytics
B. Azure Bot Service
D. Form Recognizer
NEW QUESTION 120
You have a webchat bot that provides responses from a QnA Maker knowledge base. You need to ensure that the bot uses user feedback to improve the relevance of the responses over time. What should you use?
A. key phrase extraction
B. sentiment analysis
C. business logic
D. active learning
NEW QUESTION 121
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. Choose two.)
A. QnA Maker
B. Azure Bot Service
C. Form Recognizer
D. Anomaly Detector
NEW QUESTION 122
In which two scenarios can you use a speech synthesis solution? (Each correct answer presents a complete solution. Choose two.)
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
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Not C: Extracting key phrases is not speech synthesis.
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.
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.
NEW QUESTION 125
Drag and Drop
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.
NEW QUESTION 126
Drag and Drop
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.)
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.
NEW QUESTION 127
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