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>> AI-901 Valid Braindumps Sheet <<
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NEW QUESTION # 48
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Correct selection: natural language processing
The correct answer is natural language processing . Interactive question answering requires an application to receive language from a user, interpret the meaning or intent of that language, and generate or retrieve an appropriate response. These are core capabilities associated with natural language processing (NLP) and modern generative AI systems.
Microsoft's current Azure AI Fundamentals study guide places text-oriented capabilities under AI workloads
, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
. It also requires candidates to recognize scenarios involving text processing and to implement AI solutions that work with textual user input.
Computer vision is concerned with understanding or generating visual information such as images and video.
It would therefore apply to tasks such as image classification, object detection, OCR-related visual processing, or image interpretation-not ordinary text-based question answering.
Forecasting predicts future numerical or categorical values from historical data, such as future sales, demand, or resource usage. It does not describe conversational interaction with users.
Therefore, an application that interactively receives questions expressed in natural language and returns answers is fundamentally performing a natural language processing workload .
Study Guide Reference: Identify AI concepts and capabilities # Identify AI workloads # text analysis and generative AI scenarios.
NEW QUESTION # 49
What are two types of generative Al models? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Answer: A,D
Explanation:
GPT and DALL-E are examples of generative AI model types/families.
GPT models generate natural language text, such as answers, summaries, emails, and conversations.
DALL-E models generate images from text descriptions.
Why the other options are incorrect:
A). transformer is a model architecture, not the best answer here as a generative model type/family.
B). computer vision is an AI workload area.
E). natural language processing is an AI workload area, not a specific generative AI model type.
NEW QUESTION # 50
You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?
Answer: A
Explanation:
To extract a transcript and structured information from voicemail recordings, you should use an audio analyzer.In Azure Content Understanding, analyzers are configured based on the type of data being processed. Since voicemail recordings are conversational audio content, an audio analyzer is designed to handle speech-to-text transcription, speaker labeling, and extracting the relevant structured fields.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/audio/overview
NEW QUESTION # 51
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: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.
Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement .
Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system's output. = Yes Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.
NEW QUESTION # 52
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, a summarization model is not used to transcribe spoken audio into written text; instead, an automatic speech recognition (ASR) or speech-to-text model performs the transcription, while a summarization model is used afterward to shorten the resulting text.
Box 2: No
No, a standard chat completion model cannot directly generate vector embeddings for semantic tasks. While both types of models process natural language, they are distinct architectures optimized for entirely different outputs:
Chat Completion Models (e.g., gpt-4o, Claude 3.5 Sonnet) are generative. They take a text prompt and predict the next sequence of words to produce a conversational text response.
Embedding Models (e.g., text-embedding-3-small) are extractive. They compress text into a fixed- length numerical array (a vector) that mathematically represents the underlying meaning, or semantic context, of that text.
Box 3: Yes
Yes, an embedding model can generate vector representations of the provided input. It turns text, words, or images into a list of numbers. These numbers capture the meaning of the data.
How Embeddings Work
An embedding model maps complex data into a simple numeric space.
Inputs: Words, sentences, whole documents, or images.
Outputs: A dense vector of floating-point numbers.
Meaning: Similar inputs get vectors that sit close to each other in that space.
Reference:
https://aimultiple.com/speech-recognition
https://www.debutinfotech.com/blog/understanding-the-role-of-embedding-in-models-like-chat-gpt
NEW QUESTION # 53
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