Microsoft AI-900 Korean Actual Tests : Microsoft Azure AI Fundamentals (AI-900 Korean Version)

Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify guiding principles for responsible AI", responsible AI is built upon six foundational principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle serves to guide the ethical design, deployment, and management of artificial intelligence systems.
* Fairness - This principle ensures that AI systems treat all people fairly and do not discriminate based on personal attributes such as gender, race, or age. The Microsoft Learn content emphasizes that "AI systems should treat everyone fairly" and that organizations must evaluate datasets and model outputs for bias. In this scenario, "The system must not discriminate based on gender, race" clearly aligns with Fairness because it directly addresses equitable treatment and unbiased decision-making.
* Privacy and Security - Microsoft's responsible AI framework stresses that "AI systems must be secure and respect privacy." This means personal data should be safeguarded, processed lawfully, and visible only to authorized users. The statement "Personal data must be visible only to approved users" reflects the importance of protecting sensitive information and controlling access-precisely the intent of the Privacy and Security principle.
* Transparency - Transparency refers to ensuring that users understand how AI systems operate and make decisions. Microsoft notes that "AI systems should be understandable and users should be able to know why decisions are made." The requirement "Automated decision-making processes must be recorded so that approved users can identify why a decision was made" directly supports this principle.
Transparency promotes trust and accountability by documenting the reasoning behind AI outputs.
Reliability and Safety, though another core principle, does not directly relate to any of the provided statements in this question.

Correct Answer: D  🗳️

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Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) study guide, the three main types of supervised and unsupervised machine learning models-classification, clustering, and regression-are used for distinct problem types depending on the structure of the data and the prediction goal.
* Clustering is an unsupervised learning technique used when the goal is to group items with similar characteristics without predefined labels. In this scenario, "Assign categories to passengers based on demographic data" implies automatically grouping passengers based on patterns such as age, income, or travel frequency, without any prior labeling. This directly maps to clustering, which discovers hidden groupings (for example, segmenting customers into categories like business travelers or vacationers).
* Regression is a supervised learning method used to predict continuous numerical values. The scenario
"Predict the amount of consumed fuel based on flight distance" is a classic regression problem because the output (fuel consumption) is a continuous variable dependent on another continuous variable (distance). Regression models, such as linear regression, are trained to estimate numeric outputs.
* Classification is also a supervised learning approach, but it predicts discrete categories or outcomes.
The scenario "Predict whether a passenger will miss their flight based on demographic data" involves a binary decision (missed or not missed), which is typical of classification tasks. These models learn from labeled examples to assign new instances to specific categories.
In summary, Clustering groups similar passengers, Regression predicts continuous numerical outcomes, and Classification determines categorical outcomes. This alignment precisely matches the definitions in Microsoft' s AI-900 learning objectives under "Describe common machine learning types and scenarios."

Correct Answer:

Explanation:

The correct completion of the sentence is:
"You can use the Custom Vision service to train an object detection model by using your own images." According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of computer vision workloads," the Azure Custom Vision service is a specialized component of Azure Cognitive Services for Vision that enables developers to train custom image classification or object detection models using their own labeled image datasets.
The Custom Vision service differs from the Computer Vision service in that it allows full customization - meaning you can upload your own images, tag them manually, and train the model to recognize objects specific to your use case (for example, detecting your company's products, tools, or vehicles). Once trained, the model can identify and localize these objects in new images by returning bounding boxes and confidence scores, which is precisely what defines an object detection workload.
Microsoft's AI-900 materials describe object detection as the process of identifying objects in an image and determining their position, typically represented by bounding boxes. Custom Vision supports two main project types:
* Image Classification: Determines what is present in the image (e.g., "dog," "cat," "car").
* Object Detection: Identifies what is present and where it is located in the image.
In contrast:
* Computer Vision provides prebuilt models for general image analysis but doesn't allow custom model training.
* Form Recognizer is used for extracting text and data from structured or semi-structured documents.
* Azure Video Analyzer for Media focuses on video content analysis, not custom object detection.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn content, the verified and correct answer is Custom Vision, as it specifically allows training of a custom object detection model using your own images.

Correct Answer: C  🗳️

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