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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Format: | Match the service to use case, Scenario-based, Drag and drop, Multiple select, Multiple choice |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Available Languages: | Japanese, Arabic (Saudi Arabia), German, French, Indonesian (Indonesia), Spanish, Italian, Korean, English, Chinese (Simplified), Russian, Portuguese (Brazil), Chinese (Traditional) |
| Exam Price: | USD 99 |
| Exam Duration: | 45-60 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 / 1000 |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
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NEW QUESTION # 37
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Images can be sent to these models either by passing a publicly accessible image URL or by sending Base64-encoded image data directly in the message payload.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/foundry-models/how-to/use-chat-multi-modal
NEW QUESTION # 38
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 # 39
Which type of Azure Al workload should you use to create illustrations based on the text of an article?
Answer: C
Explanation:
Creating new illustrations based on the text of an article is a content generation task. Because the AI solution must create new visual content from a text prompt or written input, the correct workload is generative AI .
NEW QUESTION # 40
Match the principles of responsible Al to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The system must not discriminate based on gender, race, or age. = Fairness This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.
Personal data must be visible only to approved users. = Privacy and security This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.
Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.
NEW QUESTION # 41
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 # 42
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