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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 700 |
| Real Exam Qty: | 30-40 |
| Exam Price: | $99 USD |
| Exam Format: | Multiple choice, Multiple select, Case studies |
| Exam Duration: | 60 minutes |
| Available Languages: | Arabic (Saudi Arabia), Chinese (Simplified), Korean, Chinese (Traditional), Spanish, Portuguese (Brazil), Japanese, Russian, German, Indonesian (Indonesia), English, French, Italian |
| Recommended Training: | Microsoft Learn: AI concepts for developers and technology professionals Microsoft Learn: Get started with Microsoft Foundry |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| 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 # 52
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: A
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema . In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms.
Microsoft's quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.
Why the other options are incorrect:
A). transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.
B). OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.
D). Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.
Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools .
NEW QUESTION # 53
You need to convert written customer notifications into natural-sounding spoken audio that can be played over a phone system. Which Azure Speech in Foundry Tools capability should you use?
Answer: A
Explanation:
The requirement is to convert written customer notifications into natural-sounding spoken audio.
This is speech synthesis, also known as text to speech.
Microsoft's Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis.
NEW QUESTION # 54
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability.
Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.
The other options are incorrect:
fairness focuses on avoiding bias and treating people fairly.
privacy and security focuses on protecting data and restricting access.
transparency focuses on explaining AI use, capabilities, and limitations.
NEW QUESTION # 55
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
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.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 56
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?
Answer: B
Explanation:
To minimize cost and keep generated responses within a defined length, configure Max Completion Tokens.
Microsoft's Azure OpenAI / Foundry API reference defines max_completion_tokens as an upper bound for the number of tokens that can be generated for a completion. Because generated tokens contribute to usage and response length, limiting completion tokens helps control both output length and cost.
Temperature and Top P control randomness or sampling behavior, not maximum response length. Model version settings do not directly define the generated response length.
NEW QUESTION # 57
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