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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Duration: | 45-60 |
| Exam Price: | USD 99 |
| Passing Score: | 700 / 1000 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Exam Format: | Drag and drop, Scenario-based, Multiple select, Match the service to use case, Multiple choice |
| Available Languages: | Spanish, Chinese (Simplified), Portuguese (Brazil), Indonesian (Indonesia), Italian, French, Arabic (Saudi Arabia), German, English, Russian, Japanese, Korean, Chinese (Traditional) |
| Real Exam Qty: | 40-60 |
| 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 |
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NEW QUESTION # 118
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations.
Which Azure speech class should you use to configure the connection to the Azure Speech service?
Answer: B
NEW QUESTION # 119
Stating the source of the data used to train a model is an example of which responsible Al principle?
Answer: D
Explanation:
The correct answer is A. transparency . In Microsoft's Responsible AI framework, transparency requires that people can understand relevant aspects of how an AI system was created, how it operates, what information it relies upon, and why it produces particular outcomes. The current Microsoft Azure AI Fundamentals study guide specifically includes "Describe considerations for transparency in an AI solution" under the responsible AI objectives.
Identifying and documenting the source of training data directly supports transparency because it establishes data provenance. Stakeholders can determine where the model's knowledge originated, assess the appropriateness and limitations of the dataset, investigate potential biases, and better understand factors that may influence model behaviour. Microsoft also describes transparency as helping stakeholders understand how and why AI systems work , with interpretability and information about datasets and model characteristics contributing to trust and auditability.
Privacy and security concerns protecting data and controlling its use. Reliability and safety concerns predictable, robust operation. Fairness focuses on avoiding systematically unequal outcomes across groups.
Merely disclosing where training data came from is therefore principally a transparency practice.
Study Guide Reference: Describe principles of responsible AI # Describe considerations for transparency in an AI solution.
NEW QUESTION # 120
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Computer vision
Computer vision uses Optical Character Recognition (OCR) and deep learning models to detect, localize, and extract printed or handwritten text from images, turning visual pixels into editable digital data.
Reference:
https://docs.cloud.google.com/vision/docs/ocr
NEW QUESTION # 121
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 # 122
Hotspot Question
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that uses the Azure OpenAI client library to send prompts to the model.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box: responses
The correct method call uses the OpenAI Responses API, which utilizes
client.responses.create(). This modern endpoint is designed for text and structured generations, moving away from the older chat.completions.create syntax.
Example code:
import os
from openai import OpenAI
# Initialize the client (automatically uses OPENAI_API_KEY environment variable) client = OpenAI()
# Call the Responses API
response = client.responses.create(
model="gpt-4o",
input="Explain quantum computing in one sentence."
)
Reference:
https://developers.openai.com/api/docs/guides/text
NEW QUESTION # 123
......
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