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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 2: Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Topic 3: Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
|
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Topic 5: Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
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NEW QUESTION # 97
Based on the image provided, here is the transcribed text:
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: C
Explanation:
The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.
NEW QUESTION # 98
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 # 99
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: D
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 # 100
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:
NEW QUESTION # 101
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .
NEW QUESTION # 102
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