Exam AI-900 topic 1 question 167 discussion

Actual exam question from Microsoft's AI-900

Question #: 167
Topic #: 1

DRAG DROP
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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.

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Suggested Answer:

by XtraWest at April 10, 2023, 9:57 a.m.

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XtraWest

Highly Voted 1 year, 1 month ago

Correct as per Bing AI

upvoted 5 times

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zellck

Most Recent 11 months, 2 weeks ago

1. Computer vision 2. Natural language processing 3. Anomaly detection 4. Machine learning (Clustering) https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr OCR or Optical Character Recognition is also referred to as text recognition or text extraction. Machine-learning based OCR techniques allow you to extract printed or handwritten text from images, such as posters, street signs and product labels, as well as from documents like articles, reports, forms, and invoices. The text is typically extracted as words, text lines, and paragraphs or text blocks, enabling access to digital version of the scanned text. This eliminates or significantly reduces the need for manual data entry.

upvoted 4 times

zellck

11 months, 2 weeks ago

https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.

upvoted 1 times

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zellck

11 months, 2 weeks ago

https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/sentiment-opinion-mining/overview#sentiment-analysis The sentiment analysis feature provides sentiment labels (such as "negative", "neutral" and "positive") based on the highest confidence score found by the service at a sentence and document-level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral and negative sentiment. https://learn.microsoft.com/en-us/training/modules/create-clustering-model-azure-machine-learning-designer/2-clustering-scenarios Clustering is a form of machine learning that is used to group similar items into clusters based on their features. For example, a researcher might take measurements of penguins, and group them based on similarities in their proportions.

upvoted 1 times

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rdemontis

1 year ago

correct answers

upvoted 3 times

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