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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Price: | USD 99 |
| Exam Format: | Scenario-based, Match the service to use case, Multiple select, Multiple choice, Drag and drop |
| Exam Duration: | 45-60 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Passing Score: | 700 / 1000 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Available Languages: | English, Korean, Chinese (Traditional), Japanese, Indonesian (Indonesia), Arabic (Saudi Arabia), French, Spanish, Chinese (Simplified), Russian, Portuguese (Brazil), Italian, German |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
>> AI-901 Training Questions <<
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NEW QUESTION # 93
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:
NEW QUESTION # 94
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?
Answer: C
NEW QUESTION # 95
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:
NEW QUESTION # 96
What is an example of a Microsoft responsible AI principle?
Answer: C
Explanation:
Correct:
* AI systems should be secure and respect privacy.
* AI systems should treat people fairly.
Incorrect:
* AI systems should be in the public domain.
* AI systems should make personal details accessible.
* AI systems should NOT reveal the details of their design.
* AI systems should protect the interests of developers.
* AI systems should use black-box models.
Note:
Responsible AI principles
*-> Fairness: AI systems should treat all people fairly.
AI systems should treat everyone equally and provide the same recommendations to all individuals. Fairness in AI systems prevents discrimination based on personal characteristics.
* Reliability and safety: AI systems should perform reliably and safely.
*-> Privacy and security: AI systems should be secure and respect privacy.
* Inclusiveness: AI systems should empower everyone and engage people.
* Etc.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
NEW QUESTION # 97
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: Yes
Yes, limiting access to AI systems and data directly reduces the risk of unauthorized data exposure by minimizing attack surfaces and preventing accidental leaks.
Key Security Benefits
Fewer Entry Points: Restricting who and what can connect to your AI model lowers the chance of a security breach.
Controlled Data Flow: Limiting data inputs ensures sensitive user or corporate information does not enter training pipelines or public logs.
Better Compliance: Strict access rules help meet privacy laws and internal security policies.
Box 2: No
Sharing personal user data does not automatically improve collaboration or support responsible AI practices; in many cases, it creates significant privacy, security, and ethical risks that run counter to responsible AI principles.
Box 3: Yes
Yes, protecting personal data and complying with regulations are essential parts of responsible AI. They build trust, respect user privacy, and prevent illegal data use.
User Trust: People want to know their private data is safe.
Legal Rules: Laws punish companies that misuse data.
Fairness: Good data habits stop AI from learning bad or biased habits.
Reference:
https://www.linkedin.com/top-content/artificial-intelligence/ai-in-cybersecurity/data-security-issues-in-artificial-intelligence/
https://pmc.ncbi.nlm.nih.gov/articles/PMC10498316/
NEW QUESTION # 98
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