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Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.
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NEW QUESTION # 88
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.
Answer:
Explanation:
Explanation:
Box 3: Natural language processing
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
NEW QUESTION # 89
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features
NEW QUESTION # 90
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.
Answer: A,C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure," conversational bots are AI applications that can understand and respond to natural language inputs through text or speech. Building such a bot typically involves two key Azure services:
* Azure Bot Service (Option B):This service provides the framework and infrastructure needed to create, test, and deploy intelligent chatbots that interact with users across multiple channels (webchat, Teams, email, etc.). It handles conversation flow, integration, and user message management.
* Azure Language Service (Option A):This service powers the natural language understanding (NLU) capability of the bot. It enables the bot to interpret user input, extract intent, and query a knowledge base using Question Answering (formerly QnA Maker). This allows the bot to respond intelligently to user questions by finding the most relevant answers.
The other options are incorrect:
* C. Form Recognizer is used for extracting structured data from documents like invoices or forms.
* D. Anomaly Detector is used for identifying unusual patterns in time-series data.
Hence, to build a bot that understands and answers user questions in natural language, the solution must combine Azure Bot Service for conversation management and Azure Language Service for knowledge-based question answering and natural language understanding.
NEW QUESTION # 91
Which term is used to describe uploading your own data to customize an Azure OpenAI model?
Answer: D
NEW QUESTION # 92
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.
Answer: A,D
Explanation:
According to the Microsoft Learn module "Explore speech capabilities of Azure AI" and the AI-900 Official Study Guide, speech synthesis (also known as text-to-speech) is the process of converting written text into spoken audio output. Azure's Speech service provides this functionality, allowing applications to produce human-like voices dynamically.
Let's evaluate each scenario:
* A. Automated voice that reads back a credit card number entered into a telephone keypad # YesThis is a classic text-to-speech (TTS) use case. The application converts numeric or textual input (such as a credit card number) into audio output that the caller hears. Azure Speech service can handle such voice responses in automated phone systems or IVR (Interactive Voice Response) setups.
* B. Generating live captions for a news broadcast # NoThis is a speech-to-text scenario (speech recognition), not speech synthesis. It involves converting audio speech into written text.
* C. Extracting key phrases from an audio recording of a meeting # NoThis involves speech-to-text followed by text analytics, not speech synthesis.
* D. An AI character in a computer game that speaks audibly to a player # YesThis is a direct example of speech synthesis, where the character's dialog text is converted into realistic spoken output for immersive interaction.
Therefore, based on Microsoft's AI-900 curriculum, speech synthesis is used in applications that convert text into audible speech, such as automated voice systems or interactive digital characters.
NEW QUESTION # 93
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