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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Exam Price: | Approx. 99 USD (varies by region) |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Related Certifications: | Microsoft Azure Fundamentals (AZ-900) Microsoft Azure Data Fundamentals (DP-900) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 60 minutes |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | Chinese (Simplified), English, Spanish, Chinese (Traditional), Portuguese (Brazil), Korean, Japanese, French, German |
| Exam Format: | Drag and drop, Multiple choice, Case study (limited), Multiple response |
| Recommended Training: | Azure AI Fundamentals Course Microsoft Learn - AI-900 Learning Path |
| Exam Registration: | Schedule exam via Pearson VUE Microsoft Certification Portal |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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NEW QUESTION # 60
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.
NEW QUESTION # 61
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
Answer: A
Explanation:
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.
Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.
No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training.
High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview
NEW QUESTION # 62
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: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services.
= No
Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to- speech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This is correct. Microsoft's guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.
NEW QUESTION # 63
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous .
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
* Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
* Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
* Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.
NEW QUESTION # 64
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?
Answer: A
Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry
NEW QUESTION # 65
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