AI-900 Korean試験無料問題集「Microsoft Azure AI Fundamentals (AI-900 Korean Version) 認定」
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
참고: 정답 하나당 1점입니다.
正解：
Explanation:
This question tests understanding of Microsoft's six guiding principles for Responsible AI, which are:
fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles, as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Responsible AI module, help ensure that AI systems are developed and used ethically and responsibly.
* Transparency - Yes:Transparency means users should understand how and why an AI system makes certain decisions. Providing an explanation of the outcome of a credit loan application clearly supports transparency because it helps customers know the reasoning behind approval or rejection. According to Microsoft Learn, transparency ensures that "AI systems are understandable by users and stakeholders," especially in sensitive applications such as finance and credit scoring. Thus, the first statement is Yes.
* Reliability and Safety - Yes:The reliability and safety principle ensures AI systems perform consistently, safely, and as intended, even in complex or high-risk environments. A triage bot that prioritizes insurance claims based on injury type aligns with this principle-it must be accurate, dependable, and safe to ensure claims are processed correctly and not influenced by errors or faulty algorithms. Microsoft teaches that AI should be "reliable under expected and unexpected conditions" to prevent harm or misjudgment. Therefore, this statement is Yes.
* Inclusiveness - No:Inclusiveness focuses on ensuring AI systems empower and benefit all users, especially those with different abilities or backgrounds. Offering an AI solution at different prices across sales territories is a business decision, not an ethical or inclusiveness principle issue. It does not relate to accessibility or equal participation of diverse users. Therefore, this final statement is No.
문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
正解：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
Feature engineering may include tasks such as:
* Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price × quantity).
* Extracting time-based components (e.g., year, month, day, hour) from datetime values.
* Encoding categorical variables (e.g., one-hot encoding or label encoding).
* Scaling or normalizing numerical features.
* Creating polynomial or interaction terms to capture complex relationships.
Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
The other options do not fit this definition:
* Feature selection is about removing redundant or irrelevant features, not generating new ones.
* Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
* Model training is the phase where the algorithm learns patterns from the data, not when features are created.
Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.
문장을 올바르게 완성하는 답을 선택하세요.
正解：
Explanation:
Privacy and security.
According to Microsoft's Responsible AI Principles, implementing filters to block harmful or inappropriate content in a Generative AI chat solution demonstrates a commitment to the Privacy and Security principle.
This principle ensures that AI systems are designed and operated in a way that protects users, their data, and society from harm.
When a chat system uses Generative AI models (like Azure OpenAI's GPT-based services), there is a risk that the model might produce unsafe, offensive, or sensitive content. Microsoft addresses this through content filters and safety systems, which automatically detect and block violent, hate-based, or sexually explicit outputs. This is part of responsible deployment practices to ensure that user interactions remain safe, private, and compliant with ethical standards.
Implementing these filters aligns with the Privacy and Security principle because it:
* Protects users from exposure to harmful or abusive content.
* Ensures that conversations are safeguarded against malicious or unsafe use.
* Upholds user trust by maintaining a safe digital environment for all participants.
Let's briefly clarify why the other options are incorrect:
* Fairness deals with ensuring unbiased treatment and equitable outcomes in AI decisions.
* Transparency focuses on explaining how AI systems make decisions.
* Accountability refers to human oversight and responsibility for AI actions.
Thus, content filtering mechanisms are explicitly an example of Privacy and Security, as they protect users and data from harm or misuse while maintaining ethical AI behavior.
Therefore, the verified correct answer is Privacy and security.
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
참고: 정답 하나당 1점입니다.
正解：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Cognitive Services documentation, the Custom Vision service is a specialized computer vision tool that allows users to build, train, and deploy custom image classification and object detection models. It is part of the Azure Cognitive Services suite, designed for scenarios where pre-built Computer Vision models do not meet specific business requirements.
* "The Custom Vision service can be used to detect objects in an image." # YesThis statement is true.
The Custom Vision service supports object detection, enabling the model to identify and locate multiple objects within a single image using bounding boxes. For example, it can locate cars, products, or animals in photos.
* "The Custom Vision service requires that you provide your own data to train the model." # YesThis statement is true. Unlike pre-trained models such as the standard Computer Vision API, the Custom Vision service requires users to upload and label their own images. The system uses this labeled dataset to train a model specific to the user's scenario, improving accuracy for custom use cases.
* "The Custom Vision service can be used to analyze video files." # NoThis statement is false. The Custom Vision service works only with static images, not videos. To analyze video files, Azure provides Video Indexer and Azure Media Services, which are designed for extracting insights from moving visual content.
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
참고: 정답 하나당 1점입니다.
正解：
Explanation:
This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules "Explore natural language processing" and "Explore speech capabilities." These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.
* You can use the Speech service to transcribe a call to text # YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains:
"The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files." This makes it ideal for call transcription, voice assistants, and meeting captioning.
* You can use the Text Analytics service to extract key entities from a call transcript # YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: "Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data."
* You can use the Speech service to translate the audio of a call to a different language # YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language.
Microsoft Learn describes this as: "Speech Translation combines speech recognition and translation to translate spoken audio to another language."