AI-900-KR 문제 101
자연어 처리를 사용하여 사용자가 지식 기반을 쿼리할 수 있는 봇을 개발할 계획입니다.
솔루션에 어떤 두 가지 서비스를 포함해야 할까요? 정답은 각각 솔루션의 일부를 나타냅니다.
참고: 정답 하나당 1점입니다.
솔루션에 어떤 두 가지 서비스를 포함해야 할까요? 정답은 각각 솔루션의 일부를 나타냅니다.
참고: 정답 하나당 1점입니다.
정답: A,B
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure," conversational bots are AI applications that can understand and respond to natural language inputs through text or speech. Building such a bot typically involves two key Azure services:
* Azure Bot Service (Option B):This service provides the framework and infrastructure needed to create, test, and deploy intelligent chatbots that interact with users across multiple channels (webchat, Teams, email, etc.). It handles conversation flow, integration, and user message management.
* Azure Language Service (Option A):This service powers the natural language understanding (NLU) capability of the bot. It enables the bot to interpret user input, extract intent, and query a knowledge base using Question Answering (formerly QnA Maker). This allows the bot to respond intelligently to user questions by finding the most relevant answers.
The other options are incorrect:
* C. Form Recognizer is used for extracting structured data from documents like invoices or forms.
* D. Anomaly Detector is used for identifying unusual patterns in time-series data.
Hence, to build a bot that understands and answers user questions in natural language, the solution must combine Azure Bot Service for conversation management and Azure Language Service for knowledge-based question answering and natural language understanding.
* Azure Bot Service (Option B):This service provides the framework and infrastructure needed to create, test, and deploy intelligent chatbots that interact with users across multiple channels (webchat, Teams, email, etc.). It handles conversation flow, integration, and user message management.
* Azure Language Service (Option A):This service powers the natural language understanding (NLU) capability of the bot. It enables the bot to interpret user input, extract intent, and query a knowledge base using Question Answering (formerly QnA Maker). This allows the bot to respond intelligently to user questions by finding the most relevant answers.
The other options are incorrect:
* C. Form Recognizer is used for extracting structured data from documents like invoices or forms.
* D. Anomaly Detector is used for identifying unusual patterns in time-series data.
Hence, to build a bot that understands and answers user questions in natural language, the solution must combine Azure Bot Service for conversation management and Azure Language Service for knowledge-based question answering and natural language understanding.
AI-900-KR 문제 102
머신 러닝에서 회귀 모델의 예는 무엇입니까?
정답: C
rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.
In machine learning, regression is a supervised learning technique used to predict continuous numeric values.
Microsoft's AI-900 study guide defines regression models as those that estimate relationships between variables-predicting a continuous outcome variable from one or more input features.
In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.
Other options represent different ML workloads:
* A involves segmentation by categories (classification or clustering).
* B represents clustering, grouping similar items without predefined labels.
* D represents computer vision, counting animals in images rather than predicting a numeric value.
Hence, the verified answer is C. Regression.
In machine learning, regression is a supervised learning technique used to predict continuous numeric values.
Microsoft's AI-900 study guide defines regression models as those that estimate relationships between variables-predicting a continuous outcome variable from one or more input features.
In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.
Other options represent different ML workloads:
* A involves segmentation by categories (classification or clustering).
* B represents clustering, grouping similar items without predefined labels.
* D represents computer vision, counting animals in images rather than predicting a numeric value.
Hence, the verified answer is C. Regression.
AI-900-KR 문제 103
머신 러닝의 발전을 위해, 훈련과 평가를 위해 데이터를 어떻게 분할해야 할까요?
정답: B
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data The correct answer is B. Randomly split the data into rows for training and rows for evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe fundamental principles of machine learning on Azure", the process of developing a machine learning model involves dividing the available dataset into two or more parts-commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.
The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.
Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image).
Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.
The AI-900 materials often illustrate this using Azure Machine Learning's data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.
Therefore, the verified and correct approach, as per Microsoft's official guidance, is B. Randomly split the data into rows for training and rows for evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe fundamental principles of machine learning on Azure", the process of developing a machine learning model involves dividing the available dataset into two or more parts-commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.
The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.
Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image).
Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.
The AI-900 materials often illustrate this using Azure Machine Learning's data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.
Therefore, the verified and correct approach, as per Microsoft's official guidance, is B. Randomly split the data into rows for training and rows for evaluation.
AI-900-KR 문제 104
웹사이트용 챗봇을 개발해야 합니다. 챗봇은 다음 문서의 정보를 기반으로 사용자의 질문에 답변해야 합니다.
Microsoft Word 문서의 제품 문제 해결 가이드
웹페이지의 자주 묻는 질문(FAQ) 목록
어떤 서비스를 이용해 문서를 처리해야 할까요?
Microsoft Word 문서의 제품 문제 해결 가이드
웹페이지의 자주 묻는 질문(FAQ) 목록
어떤 서비스를 이용해 문서를 처리해야 할까요?
정답: D
QnA Maker is an Azure Cognitive Service used to build question-and-answer knowledge bases from structured and unstructured documents, such as FAQs, product manuals, or webpages. According to the AI-
900 study guide and Microsoft Learn module "Build a knowledge base with QnA Maker", this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.
In this scenario, you have:
* A product troubleshooting guide (Word document)
* A FAQ webpage
QnA Maker can automatically read both sources, extract relevant Q & A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.
To clarify the other options:
* A. Azure Bot Service provides the chatbot interface and conversation logic but doesn't extract knowledge from documents.
* B. Language Understanding (LUIS) identifies intents and entities in natural language input, but it's not used to read document content.
* C. Text Analytics is used for key phrase extraction and sentiment analysis, not Q & A creation.
Therefore, the correct service for processing FAQ-style and document-based content into a question- answering bot is QnA Maker.
Reference:Microsoft Learn - Build a QnA Maker knowledge base from existing documents (AI-900 Learning Path)
900 study guide and Microsoft Learn module "Build a knowledge base with QnA Maker", this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.
In this scenario, you have:
* A product troubleshooting guide (Word document)
* A FAQ webpage
QnA Maker can automatically read both sources, extract relevant Q & A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.
To clarify the other options:
* A. Azure Bot Service provides the chatbot interface and conversation logic but doesn't extract knowledge from documents.
* B. Language Understanding (LUIS) identifies intents and entities in natural language input, but it's not used to read document content.
* C. Text Analytics is used for key phrase extraction and sentiment analysis, not Q & A creation.
Therefore, the correct service for processing FAQ-style and document-based content into a question- answering bot is QnA Maker.
Reference:Microsoft Learn - Build a QnA Maker knowledge base from existing documents (AI-900 Learning Path)
AI-900-KR 문제 105
문장을 올바르게 완성하는 답을 선택하세요.
정답:
Explanation:
validation.
In the Microsoft Azure AI Fundamentals (AI-900) study materials, a key concept in machine learning model development is splitting data into subsets for training, validation, and testing. A randomly extracted subset of data from a dataset is most commonly used for validation - that is, for evaluating the performance of the model during or after training.
Here's how this process works:
* Training set - This portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and parameters from this data.
* Validation set - This is a randomly selected subset (separate from training data) used to fine-tune model hyperparameters and evaluate how well the model generalizes to unseen data. It helps detect overfitting
- when the model performs well on training data but poorly on new data.
* Test set - A final, untouched dataset used to measure the model's real-world performance after all training and tuning are complete.
By reserving a random subset for validation, data scientists ensure that the model's performance metrics reflect generalization, not memorization of the training data.
Let's review the incorrect options:
* Algorithms - These are the mathematical frameworks or methods used to build models (e.g., decision trees, neural networks). They are not data subsets.
* Features - These are input variables (attributes) used by the model, not randomly selected data subsets.
* Labels - These are target values or outcomes the model predicts; again, not data subsets.
Therefore, in alignment with Azure AI-900's machine learning fundamentals, the correct completion is:
# "A randomly extracted subset of data from a dataset is commonly used for validation of the model."
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