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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Real Exam Qty: | 30-40 |
| Available Languages: | Chinese (Simplified), Korean, Spanish, German, Arabic (Saudi Arabia), French, Indonesian (Indonesia), Portuguese (Brazil), Chinese (Traditional), Japanese, Italian, Russian, English |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 700 |
| Exam Format: | Multiple select, Multiple choice, Case studies |
| Exam Price: | $99 USD |
| Recommended Training: | Microsoft Learn: Get started with Microsoft Foundry Microsoft Learn: AI concepts for developers and technology professionals |
| 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 # 39
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.
Which type of natural language processing was performed?
Answer: D
Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition, also known as Named Entity Recognition (NER). NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.
NEW QUESTION # 40
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: D,E,F
Explanation:
Microsoft's Six Responsible AI Principles
[C] Fairness: AI systems should treat all people equitably and avoid bias or discrimination.
[D] Reliability and Safety: Systems must operate dependably, consistently, and safely without causing harm.
Privacy and Security: AI must protect user data securely and respect personal privacy.
[E] Inclusiveness: Design should empower and engage diverse communities and users of all abilities.
Transparency: AI behavior and decision-making processes should be understandable to users.
Accountability: Creators and deployers of AI are responsible for how their systems operate.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 41
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?
Answer: A
Explanation:
For this scenario, you should use the Speech-to-Text Batch Transcription feature in Azure AI Speech. It is specifically built to asynchronously transcribe large volumes of prerecorded audio files from Azure Blob Storage concurrently, reducing your overall turnaround time.
To address the requirement for multiple languages without knowing the language of each call in advance, you should configure the service to use an Automatic Multilingual Configuration (a multilingual model). This allows the Speech service to automatically detect and transcribe multiple spoken languages without requiring you to split files or submit language-specific requests.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/audio/overview
NEW QUESTION # 42
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 # 43
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: The Temperature parameter can be set before deploying a model. = No temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.
Statement 2: During inference, the model name is used to route requests to a specific deployment. = No In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name , even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.
Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.
NEW QUESTION # 44
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