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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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NEW QUESTION # 126
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.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 127
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:
Explanation:
NEW QUESTION # 128
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:
Explanation:
Box 1: Yes
Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.
Box 2: Yes
The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.
Box 3: No
Custom Vision service can be used only on graphic files.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
NEW QUESTION # 129
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 130
You have the following apps:
* App1: Understands the public perception of a brand or topic
* App2: Applies profanity filters to speech-to-text
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
App1: "Understands the public perception of a brand or topic" # Sentiment analysis According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn's Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.
For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.
App2: "Applies profanity filters to speech-to-text" # Language detection The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.
Other options such as Captioning or Named Entity Recognition (NER) are not relevant:
* Captioning describes images or videos, not speech filtering.
* NER identifies people, locations, or organizations but does not handle profanity or language detection.
Therefore, based on Azure AI NLP features:
* App1 uses Sentiment analysis
* App2 uses Language detection
NEW QUESTION # 131
......
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