AI-900-KR 문제 6
어떤 시나리오가 웹 채팅 봇의 예인가요?
정답: D
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, a webchat bot is defined as a conversational AI application that interacts with users through a web-based chat interface. It simulates human conversation using text (and sometimes voice) to answer questions, assist with transactions, or provide information automatically. Microsoft Learn's "Describe features of common AI workloads" module highlights conversational AI as a major AI workload, where bots and virtual agents are used to provide automated, intelligent responses in real time through web, mobile, or messaging platforms.
In this scenario, the chatbot on the festival website provides immediate answers about scheduled events and ticket purchases. This aligns exactly with how a webchat bot operates - interacting with users through a website, handling repetitive inquiries, and providing consistent information without human intervention. This type of solution is commonly built using Azure Bot Service integrated with Azure Cognitive Services for Language, which allows the bot to understand user intent and respond naturally.
Let's examine the other options to reinforce why D is correct:
* A describes a text analytics or sentiment analysis scenario, not a conversational bot, because it classifies text sentiment but doesn't "chat" with a user.
* B is an example of machine translation using the Translator service, not a chatbot.
* C is an email classification or natural language processing task, not a webchat interaction.
The AI-900 exam objectives clearly distinguish conversational AI from other cognitive services such as translation or sentiment analysis. Conversational AI focuses on dialogue and interaction through natural language conversation channels like websites or messaging apps.
Therefore, the verified and officially aligned answer is D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
In this scenario, the chatbot on the festival website provides immediate answers about scheduled events and ticket purchases. This aligns exactly with how a webchat bot operates - interacting with users through a website, handling repetitive inquiries, and providing consistent information without human intervention. This type of solution is commonly built using Azure Bot Service integrated with Azure Cognitive Services for Language, which allows the bot to understand user intent and respond naturally.
Let's examine the other options to reinforce why D is correct:
* A describes a text analytics or sentiment analysis scenario, not a conversational bot, because it classifies text sentiment but doesn't "chat" with a user.
* B is an example of machine translation using the Translator service, not a chatbot.
* C is an email classification or natural language processing task, not a webchat interaction.
The AI-900 exam objectives clearly distinguish conversational AI from other cognitive services such as translation or sentiment analysis. Conversational AI focuses on dialogue and interaction through natural language conversation channels like websites or messaging apps.
Therefore, the verified and officially aligned answer is D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
AI-900-KR 문제 7
자동차 사이의 거리를 추정하려면 이미지에서 자동차의 위치를 파악해야 합니다.
어떤 유형의 컴퓨터 비전을 사용해야 합니까?
어떤 유형의 컴퓨터 비전을 사용해야 합니까?
정답: B
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like " indoor " , which can ' t be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like " indoor " , which can ' t be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
AI-900-KR 문제 8
문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
정답:
Explanation:
According to Microsoft's Responsible AI principles, one of the key guiding values is Reliability and Safety, which ensures that AI systems operate consistently, accurately, and safely under all intended conditions. The AI-900 study materials and Microsoft Learn modules explain that an AI system must be trustworthy and dependable, meaning it should not produce results when the input data is incomplete, corrupted, or significantly outside the expected range.
In the given scenario, the AI system avoids providing predictions when important fields contain unusual or missing values. This behavior demonstrates reliability and safety because it prevents the system from making unreliable or potentially harmful decisions based on bad or insufficient data. Microsoft emphasizes that AI systems must undergo extensive validation, testing, and monitoring to ensure stable performance and predictable outcomes, even when data conditions vary.
The other options do not fit this scenario:
* Inclusiveness ensures that AI systems are accessible to and usable by all people, regardless of abilities or backgrounds.
* Privacy and Security focuses on protecting user data and ensuring it is used responsibly.
* Transparency involves making AI decisions explainable and understandable to humans.
Only Reliability and Safety directly address the concept of an AI system refusing to act or returning an error when it cannot make a trustworthy prediction. This principle helps prevent inaccurate or unsafe outputs, maintaining confidence in the system's integrity.
Therefore, ensuring an AI system does not produce predictions when input data is incomplete or unusual aligns directly with Microsoft's Reliability and Safety principle for responsible AI.
AI-900-KR 문제 9
여러분은 소매점의 이미지를 처리하고 경쟁사의 제품을 식별하는 도구를 만들고 있습니다.
솔루션은 회사에서 제공한 이미지를 기반으로 학습되어야 합니다.
어떤 Azure AI 서비스를 사용해야 하나요?
솔루션은 회사에서 제공한 이미지를 기반으로 학습되어야 합니다.
어떤 Azure AI 서비스를 사용해야 하나요?
정답: A
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn documentation, Azure AI Custom Vision is specifically designed for training custom image classification and object detection models using images that a company provides. In this scenario, the company wants to identify competitor products from images captured in retail stores - a classic use case for custom image classification or object detection, depending on whether you are labeling entire images or identifying multiple items within an image.
Azure AI Custom Vision allows users to:
* Upload their own labeled training images.
* Train a model that learns to recognize specific objects (in this case, competitor products).
* Evaluate, iterate, and deploy the model as an API endpoint for real-time inference.
This fits perfectly with the requirement that the solution "must be trained on images provided by your company." The key phrase here indicates the need for a custom-trained model rather than a prebuilt one.
The other options are not suitable for this scenario:
* B. Azure AI Computer Vision provides prebuilt models for general-purpose image understanding (e.g., detecting common objects, reading text, describing scenes). It is not intended for training on custom datasets.
* C. Face service is limited to detecting and recognizing human faces; it cannot be trained to identify products.
* D. Azure AI Document Intelligence (formerly Form Recognizer) is focused on extracting structured data from documents and forms, not analyzing retail images.
Therefore, per Microsoft's official AI-900 training content, when a solution must be trained on custom company images to recognize specific products, the appropriate service is Azure AI Custom Vision.
Azure AI Custom Vision allows users to:
* Upload their own labeled training images.
* Train a model that learns to recognize specific objects (in this case, competitor products).
* Evaluate, iterate, and deploy the model as an API endpoint for real-time inference.
This fits perfectly with the requirement that the solution "must be trained on images provided by your company." The key phrase here indicates the need for a custom-trained model rather than a prebuilt one.
The other options are not suitable for this scenario:
* B. Azure AI Computer Vision provides prebuilt models for general-purpose image understanding (e.g., detecting common objects, reading text, describing scenes). It is not intended for training on custom datasets.
* C. Face service is limited to detecting and recognizing human faces; it cannot be trained to identify products.
* D. Azure AI Document Intelligence (formerly Form Recognizer) is focused on extracting structured data from documents and forms, not analyzing retail images.
Therefore, per Microsoft's official AI-900 training content, when a solution must be trained on custom company images to recognize specific products, the appropriate service is Azure AI Custom Vision.
AI-900-KR 문제 10
특정 지역의 특정 동물 종의 개체수 규모를 예측해야 합니다.
어떤 Azure Machine Learning 유형을 사용해야 합니까?
어떤 Azure Machine Learning 유형을 사용해야 합니까?
정답: B
In Azure Machine Learning, regression is a supervised machine learning technique used to predict continuous numerical values based on input data. According to the Microsoft AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Identify common types of machine learning," regression models are ideal when the goal is to estimate a quantity - such as price, temperature, or, in this case, population size.
In the scenario, the task is to predict the population size of a specific species within a defined area. Population size is a numerical, continuous value that varies depending on multiple factors (like time, environment, and resources). A regression algorithm, such as linear regression or decision tree regression, can be trained on historical data (e.g., species count, area, temperature, food availability) to forecast future population numbers.
Option analysis:
* A. Clustering: Used for unsupervised learning, where the goal is to group similar data points into clusters without predefined labels (e.g., grouping animals by behavior or habitat).
* C. Classification: Used to predict discrete categories or labels (e.g., "endangered" vs. "not endangered"), not numerical values.
Therefore, the correct machine learning type for predicting a continuous value such as population size is Regression.
In the scenario, the task is to predict the population size of a specific species within a defined area. Population size is a numerical, continuous value that varies depending on multiple factors (like time, environment, and resources). A regression algorithm, such as linear regression or decision tree regression, can be trained on historical data (e.g., species count, area, temperature, food availability) to forecast future population numbers.
Option analysis:
* A. Clustering: Used for unsupervised learning, where the goal is to group similar data points into clusters without predefined labels (e.g., grouping animals by behavior or habitat).
* C. Classification: Used to predict discrete categories or labels (e.g., "endangered" vs. "not endangered"), not numerical values.
Therefore, the correct machine learning type for predicting a continuous value such as population size is Regression.
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