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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Available Languages: | English, French, Spanish, Japanese, Korean, Chinese (Simplified), German, Chinese (Traditional), Portuguese (Brazil) |
| Passing Score: | 700 (out of 1000) |
| Exam Format: | Drag and drop, Case study (limited), Multiple choice, Multiple response |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Related Certifications: | Microsoft Azure Data Fundamentals (DP-900) Microsoft Azure Fundamentals (AZ-900) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 60 minutes |
| Exam Price: | Approx. 99 USD (varies by region) |
| Recommended Training: | Azure AI Fundamentals Course Microsoft Learn - AI-900 Learning Path |
| Exam Registration: | Microsoft Certification Portal Schedule exam via Pearson VUE |
| 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 # 37
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 # 38
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model.
To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
The other options are incorrect:
evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
Foundry playground is an interactive testing environment, not the production target for application requests.
training dataset of the model is used for training or fine-tuning, not for sending inference requests.
Therefore, the correct answer is endpoint of the model.
NEW QUESTION # 39
Which Azure OpenAI model should you use to summarize the text from a document?
Answer: A
Explanation:
You can use ChatGPT to summarize text. This can help you understand complex information more easily, summarize the central argument of your own paper, or clarify your research question.
Reference:
https://community.openai.com/t/information-summary-by-using-api/578792
NEW QUESTION # 40
Which Azure AI Foundry Service can be used to identify documents that contain sensitive information?
Answer: B
Explanation:
Azure AI Language can be used to identify documents containing sensitive information through its Personally Identifiable Information (PII) detection feature, which uses Named Entity Recognition (NER) to detect and categorize data like names, addresses, phone numbers, and financial details within unstructured text and native documents. This feature classifies sensitive data into predefined categories and can also redact the identified information, replacing it with asterisks to protect privacy.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable- information/overview
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, 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 # 42
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