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| Section | Weight | Objectives |
|---|---|---|
| Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
|
| Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
|
| Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Describe features of computer vision workloads on Azure | 15-20% | - Image analysis
|
| Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
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NEW QUESTION # 76
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?
Answer: B
Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
NEW QUESTION # 77
You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.
You need to perform speech-to-text transcription from the microphone.
Which Python class should you use?
Answer: C
Explanation:
speechsdk.SpeechRecognizer is the appropriate Python class to handle continuous real-time speech-to-text transcription from a default microphone.
How to Implement It
To achieve continuous transcription (rather than transcribing a single sentence and stopping), you instantiate this class and invoke its asynchronous long-running method.speechsdk.SpeechRecognizer: Acts as the core proxy client connecting your application to the Azure Speech service.
start_continuous_recognition(): The essential method on this class that instructs the SDK to stream microphone data indefinitely and process multi-sentence conversations.
Reference:
https://stackoverflow.com/questions/67691338/continuous-speech-recognition-from-microphone-on-ms-azure
NEW QUESTION # 78
What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Giving a generative AI model clear, structural instructions (commonly referred to as Prompt Engineering) is the standard method used to define its persona, guide its behavior, and place strict operational boundaries on its responses.This is typically achieved through explicit instruction sets like System Instructions and targeted Prompt Guidelines.
1. Defining the Agent's Role and BehaviorEstablishing a persona prevents the AI from wandering into areas outside its intended scope and ensures the delivery style matches your needs.
2. Defining Constraints on ResponsesConstraints act as guardrails that prevent the AI from hallucinating, rambling, or providing incorrect information.
Reference:
https://ai.google.dev/gemini-api/docs/prompting-strategies
NEW QUESTION # 79
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: transcription
Azure Content Understanding in Foundry Tools uses transcription preprocessing to convert spoken words from audio and video files into text-based transcripts.
Optimized Preprocessing: Transcription runs as an initial extraction step to provide rich textual context before passing data to generative AI models.
Transcript Formatting: Conversational audio is turned into searchable WebVTT transcripts containing word-level or sentence-level timestamp.
Speaker Separation: Features like diarization identify and distinguish different speakers, while speaker role detection can pinpoint specific roles (such as agent and customer).
Multilingual Handling: It supports automatic language identification and multi-language phrase transcription.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/video/overview
NEW QUESTION # 80
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:
NEW QUESTION # 81
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