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NEW QUESTION # 53
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: analyzes images to detect
A multimodal model analyzes images to detect, recognize, and reason about objects by combining computer vision with natural language processing.
Reference:
https://farukalamai.substack.com/p/object-detection-with-multimodal
NEW QUESTION # 54
You need to build an AI solution that produces new product images based on written descriptions provided by users. Which AI workload should you use?
Answer: B
Explanation:
An AI workload that creates entirely new visual content based on written natural language descriptions is defined as image generation, which is a core capability of Generative AI. In the Microsoft Azure ecosystem, this task is primarily handled by generative models like DALL-E or GPT-image via Azure OpenAI Service and Azure AI Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/ai-services/image-video-processing
NEW QUESTION # 55
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct Python method is:
recognizer.recognize_once()
Completed code:
import azure.cognitiveservices.speech as speechsdk
speech_config = speechsdk.SpeechConfig(subscription=key, region=region) recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config) recognizer.recognize_once() Microsoft's Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.
Why the other options are incorrect:
recognizer.speak_text_async( " Ready " ) is incorrect because speaking text is text-to-speech , not speech-to- text recognition.
recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.
recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to- text transcription of spoken commands.
Therefore, the correct answer is:
recognizer.recognize_once()
NEW QUESTION # 56
You are building an AI-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible AI principle?
Answer: D
Explanation:
Transparency: AI systems should be transparent and understandable. AI systems might help inform decisions that have great impact on people's lives, so it's important that people understand how these decisions are made.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
NEW QUESTION # 57
You need to convert written customer notifications into natural-sounding spoken audio that can be played over a phone system. Which Azure Speech in Foundry Tools capability should you use?
Answer: A
Explanation:
Text-to-Speech (TTS) / Speech Synthesis within Azure Speech in Foundry Tools is exactly the correct tool to use for this scenario.
Azure Speech in Foundry Tools (formerly known as Azure AI Speech) provides robust, enterprise-grade capabilities designed to convert written text into natural-sounding, humanlike synthesized speech.
Natural-Sounding Neural Voices: The tool uses advanced deep neural networks to produce fluid phrasing, realistic intonation, and high-quality audio that mimics the human voice accurately, which is essential for clear phone notifications.
Fine-Grained Customization (SSML): By using Speech Synthesis Markup Language (SSML), you can directly adjust the pitch, volume, speaking rate, pronunciation, and pauses. This allows you to fine-tune the audio specifically for telecom standards or unique brand guidelines.
Flexible Audio Formats: The tool supports multiple audio output codecs and streaming capabilities, allowing you to generate files compatible with common telephony equipment requirements (such as specific sample rates or WAV formats).
Scalability Options: You can choose between Real-time speech synthesis for on-demand interactive voice responses (IVR) or Asynchronous batch synthesis if you need to pre-render massive bulks of notification files at once.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/speech
NEW QUESTION # 58
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