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NEW QUESTION # 31
You have a Microsoft Foundry project that contains a vision-enabled GPT model deployment You need to develop an application that sends an image and a text question to the model in a single request.
What should you use?
Answer: A
Explanation:
For a vision-enabled GPT model deployment, use the Azure OpenAI Responses API to send text and image inputs in a single request. Microsoft's Foundry SDK documentation states that the endpoint exposes the Responses API and provides access to Azure OpenAI models, including chat completions and multimodal- capable model interactions.
NEW QUESTION # 32
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:
Explanation:
Statement 1: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models, not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.
NEW QUESTION # 33
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:
Explanation:
Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.
NEW QUESTION # 34
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script.
You need to generate a narration audio file for each video based on the script.
Which type of workload should you use?
Answer: C
Explanation:
Text to speech enables your applications, tools, or devices to convert text into humanlike synthesized speech. The text to speech capability is also known as speech synthesis. Use humanlike prebuilt neural voices out of the box, or create a custom neural voice that's unique to your product or brand.
Reference:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/text-to-speech
NEW QUESTION # 35
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each connect selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Using Azure Speech in Foundry Tools for batch transcription requires live audio input from a microphone. = No Batch transcription is used to process stored audio files, not live microphone input. Live microphone input is used for real-time speech recognition scenarios.
Statement 2: Azure Speech in Foundry Tools can analyze video frames to detect and label objects while transcribing audio. = No Azure Speech is for speech capabilities such as speech recognition, speech synthesis, and speech translation.
Detecting and labeling objects in video frames is a computer vision/video analysis capability, not an Azure Speech capability.
Statement 3: Speech translation in Azure Speech in Foundry Tools can translate spoken audio into text in another language. = Yes Speech translation converts spoken input in one language into translated text in another language.
NEW QUESTION # 36
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