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NEW QUESTION # 33
Hotspot Question
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:
Box 1: Yes
Yes - Whisper models in Azure OpenAI Service are used to convert speech to text.
The Whisper model is a speech to text model from OpenAI that you can use to transcribe or translate audio files. The model is trained on a large dataset of English audio and text. The model is optimized for transcribing audio files that contain speech in English.
Box 2: Yes
Yes - Embedding models in Azure OpenAI Service are used to convert text into numeric vectors.
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
Embeddings: a set of models that can convert text into numerical vector form to facilitate text similarity.
Box 3: No
GPT-4.1 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-4.1 family is GPT-4.1 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-4.1 Turbo is available for use with the Chat Completions API.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
NEW QUESTION # 34
Hotspot Question
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:
Box 1: No
No, not all AI models in Azure can analyze and generate images. Azure hosts a vast model catalog (such as through Foundry Models) containing thousands of specialized foundation models, where capabilities depend entirely on the specific model architecture.
Box 2: Yes
A multimodal generative AI model in Azure can absolutely interpret image inputs within a prompt.
These advanced systems process text and visual data simultaneously. This allows you to ask questions about an uploaded image directly in your text prompt.
Box 3: Yes
These advanced systems process text and visual data simultaneously. This allows you to ask questions about an uploaded image directly in your text prompt.
Reference:
https://learn.microsoft.com/en-us/azure/developer/ai/get-started-app-chat-vision
NEW QUESTION # 35
You ate building an application that uses Azure Speech in Foundry Tools. You need to initialize the Speech SDK for Python oy creating a SpechConfig object. Which values should you specify?
Answer: B
Explanation:
To initialize the Azure Speech SDK for Python, you create a SpeechConfig object with information for the Speech resource. Microsoft documentation states that SpeechConfig includes information such as the Speech resource key and associated region, endpoint, host, or authorization token . It also shows the Python pattern:
speech_config = speechsdk.SpeechConfig(subscription= " YourSpeechKey " , region= " YourSpeechRegion " ) or, for endpoint-based configuration:
speech_config = speechsdk.SpeechConfig(subscription=os.environ.get( " SPEECH_KEY " ), endpoint=os.
environ.get( " ENDPOINT " ))
Therefore, among the provided options, the correct answer is D. the Foundry resource key and endpoint .
Options A, B, and C are incorrect because Speech SDK initialization does not use an OpenAI model deployment name, storage account credentials, or the Foundry project name/project endpoint.
NEW QUESTION # 36
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience. Which AI workload should you use?
Answer: B
Explanation:
Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user's prompt.
NEW QUESTION # 37
You deploy an AI system to assist with hiring decisions.
Company policy requires that human reviewers oversee AI-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible AI principle is this an example of?
Answer: C
Explanation:
Accountability is the Microsoft responsible AI principle that ensures human oversight so that people remain responsible for AI-driven decisions.
Human Oversight: Requiring human review ensures that AI operates under effective governance rather than acting autonomously on high-impact choices.
Final Authority: Keeping humans legally and operationally responsible prevents automated systems from dictating critical outcomes like hiring.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 38
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