AI-900-KR 문제 76
변압기 모델의 세 단계는 무엇인가요? 각 정답은 완전한 해답을 제시합니다.
참고: 정답은 1점입니다.
참고: 정답은 1점입니다.
정답: B,C,D
A transformer model is the foundational architecture behind many modern natural language processing systems such as GPT and BERT. It processes text data through multiple key stages. According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn materials, the major stages of a transformer-based large language model are tokenization, embedding calculation, and next token prediction.
* Tokenization (C) - The first step converts raw text into smaller units called tokens (words, subwords, or characters). This process allows the model to handle text in a structured numerical form rather than as raw language.
* Embedding Calculation (B) - After tokenization, the tokens are mapped into high-dimensional numeric vectors, known as embeddings. These embeddings capture semantic relationships between words and phrases so that the model can understand context and meaning.
* Next Token Prediction (D) - This stage is the heart of transformer operation, where the model predicts the next likely token in a sequence based on prior tokens. Repeated next-token predictions enable text generation, summarization, or translation.
Options A (object detection) and E (anonymization) are incorrect because they relate to vision and privacy workflows, not language modeling.
* Tokenization (C) - The first step converts raw text into smaller units called tokens (words, subwords, or characters). This process allows the model to handle text in a structured numerical form rather than as raw language.
* Embedding Calculation (B) - After tokenization, the tokens are mapped into high-dimensional numeric vectors, known as embeddings. These embeddings capture semantic relationships between words and phrases so that the model can understand context and meaning.
* Next Token Prediction (D) - This stage is the heart of transformer operation, where the model predicts the next likely token in a sequence based on prior tokens. Repeated next-token predictions enable text generation, summarization, or translation.
Options A (object detection) and E (anonymization) are incorrect because they relate to vision and privacy workflows, not language modeling.
AI-900-KR 문제 77
기존 데이터 세트에서 훈련 데이터 세트와 검증 데이터 세트를 만들어야 합니다.
Azure Machine Learning Designer에서 어떤 모듈을 사용해야 하나요?
Azure Machine Learning Designer에서 어떤 모듈을 사용해야 하나요?
정답: C
In Azure Machine Learning designer, the Split Data module is specifically designed to divide a dataset into training and validation (or testing) subsets. The AI-900 study guide and the Microsoft Learn module "Split data for training and evaluation" explain that this module allows users to control how data is partitioned, ensuring that models are trained on one portion of the data and tested on unseen data to assess performance.
By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70-80% of the data is used for training and the remaining 20-30% for validation or testing. This ensures the model's generalizability and prevents overfitting.
The other options serve different purposes:
* A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.
* B. Add Rows: Combines multiple datasets vertically.
* D. Join Data: Combines datasets horizontally based on a common key.
Only Split Data performs the function of dividing data into training and validation subsets.
Reference:Microsoft Learn - Split data for training and evaluation in Azure Machine Learning designer
By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70-80% of the data is used for training and the remaining 20-30% for validation or testing. This ensures the model's generalizability and prevents overfitting.
The other options serve different purposes:
* A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.
* B. Add Rows: Combines multiple datasets vertically.
* D. Join Data: Combines datasets horizontally based on a common key.
Only Split Data performs the function of dividing data into training and validation subsets.
Reference:Microsoft Learn - Split data for training and evaluation in Azure Machine Learning designer
AI-900-KR 문제 78
다음 중 마이크로소프트의 책임 있는 AI 원칙의 예시는 무엇입니까?
정답: C
The correct answer is C. AI systems must be understandable, which corresponds to the Transparency principle of Microsoft's Responsible AI framework.
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI", Microsoft defines six key principles for responsible AI:
* Fairness - AI systems should treat everyone equitably.
* Reliability and safety - AI should function as intended, even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower everyone and engage diverse users.
* Transparency - AI systems should be understandable.
* Accountability - People should be accountable for AI systems.
The statement "AI systems must be understandable" aligns directly with the Transparency principle, ensuring that AI decisions and behaviors can be explained and interpreted by developers, users, and stakeholders.
Microsoft emphasizes that transparent AI builds trust, allows debugging, and ensures ethical usage.
Other options are incorrect:
* A. Use only publicly available data - Not a principle of Responsible AI.
* B. Protect the interests of the company - Focused on business goals, not ethical AI.
* D. Keep personal details public - Violates the Privacy and Security principle.
# Final Answer (Q179): C. AI systems must be understandable.
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI", Microsoft defines six key principles for responsible AI:
* Fairness - AI systems should treat everyone equitably.
* Reliability and safety - AI should function as intended, even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower everyone and engage diverse users.
* Transparency - AI systems should be understandable.
* Accountability - People should be accountable for AI systems.
The statement "AI systems must be understandable" aligns directly with the Transparency principle, ensuring that AI decisions and behaviors can be explained and interpreted by developers, users, and stakeholders.
Microsoft emphasizes that transparent AI builds trust, allows debugging, and ensures ethical usage.
Other options are incorrect:
* A. Use only publicly available data - Not a principle of Responsible AI.
* B. Protect the interests of the company - Focused on business goals, not ethical AI.
* D. Keep personal details public - Violates the Privacy and Security principle.
# Final Answer (Q179): C. AI systems must be understandable.
AI-900-KR 문제 79
사용자 프롬프트를 기반으로 이미지를 생성해야 합니다. 어떤 Azure OpenAI 모델을 사용해야 할까요?
정답: B
According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as "a futuristic city skyline at sunset", DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.
DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications-useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text-image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.
By contrast, the other options serve different purposes:
* A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.
* C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.
* D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.
Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.
DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications-useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text-image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.
By contrast, the other options serve different purposes:
* A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.
* C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.
* D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.
Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.
AI-900-KR 문제 80
AI 기반 대출 승인 앱을 만들고 있습니다.
대출이 승인되거나 거부된 이유를 앱에 문서화하고, 신청자에게 보고서를 제공해야 합니다.
이는 Microsoft의 책임 있는 AI 원칙의 예입니까?
대출이 승인되거나 거부된 이유를 앱에 문서화하고, 신청자에게 보고서를 제공해야 합니다.
이는 Microsoft의 책임 있는 AI 원칙의 예입니까?
정답: C
According to Microsoft's Responsible AI Principles, transparency refers to ensuring that AI systems are understandable and that their decisions or predictions can be explained clearly to users. When building an AI- based loan approval app, documenting the reasons for approving or rejecting a loan and making this information available to the applicant ensures that the system's decision-making process is transparent and easily interpretable.
Transparency in AI involves making the model's inputs, features, and reasoning process visible and comprehensible to both developers and users. For a loan approval application, this could mean showing which factors-such as income, credit score, or debt ratio-influenced the outcome. Microsoft emphasizes that users should be aware when AI is making decisions that affect them and should have access to an explanation of how those decisions were made.
* Fairness (A) ensures that AI systems treat all individuals equitably without bias.
* Inclusiveness (B) focuses on accessibility and ensuring AI benefits all groups.
* Accountability (D) ensures that humans remain responsible for the outcomes of AI systems.
While accountability supports ethical oversight, the specific act of explaining and documenting the AI's decision process aligns directly with transparency.
Therefore, the verified answer is C. Transparency.
Transparency in AI involves making the model's inputs, features, and reasoning process visible and comprehensible to both developers and users. For a loan approval application, this could mean showing which factors-such as income, credit score, or debt ratio-influenced the outcome. Microsoft emphasizes that users should be aware when AI is making decisions that affect them and should have access to an explanation of how those decisions were made.
* Fairness (A) ensures that AI systems treat all individuals equitably without bias.
* Inclusiveness (B) focuses on accessibility and ensuring AI benefits all groups.
* Accountability (D) ensures that humans remain responsible for the outcomes of AI systems.
While accountability supports ethical oversight, the specific act of explaining and documenting the AI's decision process aligns directly with transparency.
Therefore, the verified answer is C. Transparency.
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