Explore how Azure AI’s conversational language understanding powers voice-activated IoT control systems while maintaining enterprise-grade security across 96 languages.
Table of Contents
Question
Which of the following are valid use cases for the conversational language understanding feature in Azure AI NLP workloads? (Choose two.)
A. Returning the class of every object in a customer image as well as the bounding boxes for each object
B. Locating customer faces in images or videos and returning their bounding box coordinates
C. Enabling voice commands for controlling devices or applications
D. Handling various employee interactions such as answering FAQs, managing calendars, or collecting feedback
E. Extracting text from customer uploaded images
Answer
C. Enabling voice commands for controlling devices or applications
D. Handling various employee interactions such as answering FAQs, managing calendars, or collecting feedback
Explanation
Two valid use cases for the conversational language understanding (CLU) feature in Azure AI NLP workloads are:
- Handling various employee interactions such as answering FAQs, managing calendars, or collecting feedback.
- Enabling voice commands for controlling devices or applications.
Other use cases for the CLU feature include:
- End-to-end conversational bots: Powering chatbots in various applications such as online shopping, food ordering, and customer service.
- Human assistant bots: Assisting staff with tasks such as routing customer inquiries or guiding employees.
CLU is a feature offered by Azure AI Language that allows you to build custom models for understanding natural language (human-like) conversations. Key functionalities include:
- Predicts the intent: Identifies the overall purpose or goal behind a user’s utterance (what the user wants to achieve).
- Extracts information: Extracts important details from the utterance (e.g., names, dates, locations).
- Focuses on understanding: Provides the “intelligence” to interpret the user’s input but does not perform any actions on its own.
Returning the class of every object in a customer image as well as the bounding boxes for each object is not a valid use case for CLU. This task falls under computer vision capabilities such as object detection, not CLU which focuses on natural language understanding.
Extracting text from customer uploaded images is not a valid use case for CLU. This is handled by optical character recognition (OCR).
Locating customer faces in images or videos and returning their bounding box coordinates is not a valid use case for CLU. This is another computer vision task and is related to facial recognition.
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