Microsoft Azure AI Fundamentals (AI-900 Korean Version) - AI-900 Korean Exam Questions

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Explanation:

The correct answer is "An embedding."
In the context of large language models (LLMs) such as GPT-3, GPT-3.5, or GPT-4, an embedding refers to a multi-dimensional numeric vector representation assigned to each word, token, or phrase. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI embeddings, embeddings are used to represent textual or semantic meaning in a numerical form that a machine learning model can process mathematically.
Each embedding captures the semantic relationships between words. Words or tokens with similar meanings (for example, "car" and "automobile") are represented by vectors that are close together in the multi- dimensional space, while unrelated words (like "tree" and "laptop") are farther apart. This vector representation enables the model to understand context, similarity, and relationships between different pieces of text.
Embeddings are fundamental in tasks such as:
* Semantic search: Finding documents or sentences with similar meaning.
* Clustering: Grouping related concepts together.
* Recommendation systems: Suggesting similar content based on text meaning.
* Contextual understanding: Helping generative models produce coherent and context-aware text.
Option review:
* Attention: A mechanism used within transformers to focus on relevant parts of input sequences but not a representation of words.
* A completion: Refers to the generated text output from a model, not the internal representation.
* A transformer: The architecture that powers models like GPT, not the vector representation of tokens.
Therefore, the correct term for a multi-dimensional vector assigned to each word or token in a large language model (LLM) is An embedding, which represents how meaning is numerically encoded and compared within language models.

Explanation:
Features
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning," data values that influence the prediction of a model are called features. In the context of machine learning, a feature is an individual measurable property, attribute, or input variable used by the model to make predictions.
Features are the independent variables that describe the characteristics of the data. For example, in a housing price prediction model, features might include square footage, location, number of bedrooms, and year built.
These inputs help the model understand relationships in the data so it can predict the target outcome (the house price).
Microsoft Learn explains that features are the input variables that the algorithm uses to identify patterns and relationships in the training data. During training, the model learns how changes in these features influence the label (also known as the dependent variable or target variable). The label is the value the model tries to predict-such as "price," "category," or "yes/no." Here's how the other options differ:
* Dependent variables (labels): These are the outcomes or target values the model predicts, not the inputs.
* Identifiers: These are unique keys (like customer ID or transaction ID) used to distinguish records but not to influence predictions.
* Labels: As mentioned, labels are the results the model tries to predict.
Therefore, based on the AI-900 learning objectives and Microsoft's official explanation, the data values that influence the prediction of a model-that is, the input variables that guide the model's learning-are called features. These features form the foundation of the model's predictive capabilities and directly impact its accuracy and performance.

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation for Azure AI Custom Vision, this service is a specialized part of the Azure AI Vision family that enables developers to train custom image classification and object detection models. It allows organizations to build tailored computer vision models that recognize images or specific objects relevant to their business needs.
* Detect objects in an image # YesThe Azure AI Custom Vision service supports both image classification (assigning an image to one or more categories) and object detection (identifying and locating objects within an image using bounding boxes). This means it can indeed detect and differentiate multiple objects in a single image, making this statement true.
* Requires your own data to train the model # YesThe Custom Vision service is designed to be customizable. Unlike prebuilt Azure AI Vision models that work out of the box, Custom Vision requires you to upload and label your own dataset for training. The model then learns from your examples to perform specialized image recognition tasks relevant to your domain. Thus, this statement is also true.
* Analyze video files # NoWhile Custom Vision can analyze images, it does not directly process or analyze video files. Video analysis is handled by a different service-Azure Video Indexer-which can extract insights such as spoken words, scenes, and faces from videos.
In summary:
# Yes - Detect objects in images
# Yes - Requires your own data
# No - Does not analyze video files.

Explanation:

In the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning (AML) learning paths, deploying a real-time inference pipeline refers to making a trained machine learning model available as a web service that can process incoming data and return predictions instantly. To achieve this, the model must be deployed to an infrastructure capable of handling continuous, low-latency requests with high reliability and scalability.
Microsoft's official guidance from Azure Machine Learning documentation specifies that:
* For testing or development, you can deploy to Azure Container Instances (ACI) because it provides a lightweight, temporary environment suitable for small-scale or non-production workloads.
* For production-grade, real-time inference, the deployment should be made to Azure Kubernetes Service (AKS).
AKS provides enterprise-level scalability, load balancing, and high availability, which are critical for serving real-time predictions to multiple consumers simultaneously. It manages containerized applications using Kubernetes orchestration, allowing the model to scale automatically based on traffic demands.
Azure Machine Learning Compute is mainly used for model training and batch inference pipelines, not real- time endpoints. A local web service is typically used only for debugging or offline testing on a developer machine and cannot be shared for external consumption.
Therefore, when deploying a real-time inference pipeline as a service for others to consume, the correct and Microsoft-verified option is Azure Kubernetes Service (AKS). This environment ensures production readiness, secure endpoint management, and scalability for live AI applications, fully aligning with best practices outlined in the Azure Machine Learning designer documentation and AI-900 exam objectives.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

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