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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple select, Multiple choice, Case studies |
| Real Exam Qty: | 30-40 |
| Certificate Validity Period: | Lifetime |
| Exam Price: | $99 USD |
| Available Languages: | Indonesian (Indonesia), Spanish, French, Italian, Russian, Chinese (Traditional), Portuguese (Brazil), English, Arabic (Saudi Arabia), German, Japanese, Korean, Chinese (Simplified) |
| Passing Score: | 700 |
| Recommended Training: | Microsoft Learn: AI concepts for developers and technology professionals Microsoft Learn: Get started with Microsoft Foundry |
| Exam Registration: | Pearson VUE Scheduling Microsoft Certification Exam Registration |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/ |
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NEW QUESTION # 25
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Defining an AI system for a typical user profile is sufficient to support inclusiveness.
No
A high number of active users means an AI system provides an inclusive experience.
No
Providing accessible interfaces and language options helps support inclusiveness in an AI system.
Yes
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is No, No, Yes .
Microsoft defines inclusiveness as designing AI systems so they work effectively for the full range of people they are intended to serve. The current AI-901 study guide explicitly includes the ability to describe considerations for inclusiveness in an AI solution as part of the Responsible AI objective domain.
The first statement is No because designing only for a hypothetical or "typical" user can exclude people with different abilities, languages, interaction styles, cultural backgrounds, or accessibility requirements. Inclusive design deliberately considers diverse and potentially underserved users.
The second statement is No because the number of active users is an adoption or usage metric. A system may have millions of users while still creating accessibility barriers or excluding particular groups. Usage volume therefore does not demonstrate inclusiveness.
The third statement is Yes . Accessible interfaces, assistive-technology compatibility, and suitable language options reduce barriers and broaden the range of people who can effectively interact with an AI system.
Microsoft specifically emphasizes accessibility and supporting varied user needs as core elements of inclusive AI design.
NEW QUESTION # 26
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct Python method is:
recognizer.recognize_once()
Completed code:
import azure.cognitiveservices.speech as speechsdk
speech_config = speechsdk.SpeechConfig(subscription=key, region=region) recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config) recognizer.recognize_once() Microsoft's Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.
Why the other options are incorrect:
recognizer.speak_text_async( " Ready " ) is incorrect because speaking text is text-to-speech, not speech-to- text recognition.
recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.
recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to- text transcription of spoken commands.
Therefore, the correct answer is:
recognizer.recognize_once()
NEW QUESTION # 27
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value 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:
NEW QUESTION # 28
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: A
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 # 29
You are deploying a generative AI model to a Microsoft Foundry project.
You assign a higher tokens per minute (TPM) allocation to the model.
What is the result of this change?
Answer: D
Explanation:
A higher Tokens Per Minute (TPM) allocation to a generative AI model in a Microsoft Foundry project increases the project's maximum throughput capacity, allowing it to process more text per minute, handle concurrent users or larger prompts without throttling, and scale production workloads.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/quotas-limits
NEW QUESTION # 30
......
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