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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Exam Format: | Drag and drop, Multiple choice, Multiple response, Case study (limited) |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | Japanese, Chinese (Traditional), Chinese (Simplified), Portuguese (Brazil), French, English, German, Korean, Spanish |
| Related Certifications: | Microsoft Azure Fundamentals (AZ-900) Microsoft Azure Data Fundamentals (DP-900) |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Exam Price: | Approx. 99 USD (varies by region) |
| Recommended Training: | Azure AI Fundamentals Course Microsoft Learn - AI-900 Learning Path |
| Exam Registration: | Schedule exam via Pearson VUE Microsoft Certification Portal |
| 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 # 33
Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?
Answer: B
Explanation:
Fairness and inclusiveness in Azure Machine Learning: The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2
NEW QUESTION # 34
You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?
Answer: C
Explanation:
Microsoft's Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:
auto means the model decides whether to call tools.
required means the model must call one or more tools.
none means the model does not call tools.
Therefore:
A . auto = Incorrect, because the model may or may not call the Azure function.
B . none = Incorrect, because this prevents tool/function calls.
C . required = Correct, because it forces the agent to call a tool.
The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call.
NEW QUESTION # 35
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:
A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.
Statement 1: System prompts can be used to authorize users. = No
Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.
Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.
Statement 3: A system prompt guides the behavior of a generative AI model. = Yes This is correct. Microsoft documentation says system messages help steer the model's behavior, tone, and output format.
NEW QUESTION # 36
Capturing text from images is an example of which type of AI capability?
Answer: D
Explanation:
Capturing text from images is the exact definition of optical character recognition (OCR).
How OCR Works
Image input: Software takes a photo, scan, or graphic containing letters.
Shape analysis: It scans the pixels to find light and dark areas that match letters or numbers.
Text output: It changes the visual shapes into real, editable digital text.
Reference:
https://en.wikipedia.org/wiki/Optical_character_recognition
NEW QUESTION # 37
Which type of Azure Al workload should you use to create illustrations based on the text of an article?
Answer: C
Explanation:
Creating new illustrations based on the text of an article is a content generation task. Because the AI solution must create new visual content from a text prompt or written input, the correct workload is generative AI .
NEW QUESTION # 38
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