Microsoft Azure AI Fundamentals (AI-900 Korean Version) - AI-900 Korean FREE EXAM DUMPS QUESTIONS & ANSWERS

Explanation:
Azure Kubernetes Service (AKS).
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions.
Azure ML offers two primary options for hosting and deploying models:
* Azure Kubernetes Service (AKS) - for high-scale, production-grade deployments that require fast response times, high availability, and scalability.
* Azure Container Instances (ACI) - for testing or low-scale workloads where cost and simplicity are more important than performance.
AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time.
The other options serve different purposes:
* Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.
* Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment.
* Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models.
Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.

Explanation:

Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Explanation:

Box 1: 11

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer
/evaluate-model

Explanation:

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.
* Generate captions for images # Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn's
"Describe features of computer vision workloads," image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image's contents in human-readable language-based on what the model identifies as key visual features.
* Extract movie title names from movie poster images # Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision's Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents.
In this case, the movie title text from a poster is best extracted using OCR.
* Locate vehicles in images # Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It's ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.

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.

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.
In a supervised learning process, data is typically divided into two parts:
* Features # The input variables used by the model to learn patterns (e.g., customer age, income, credit score).
* Label (Target) # The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).
During training, the model uses the features to understand how input data correlates with the target output.
Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.
For example:
* In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.
* In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.
To contrast with other options:
* Dependent variables (or labels) are the outcomes the model predicts.
* Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.
* Labels are the target outputs, not the inputs.
Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called "features."

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service

Explanation:

In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.

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Explanation:
Description
Correct Service
Enables the use of natural language to query a knowledge base.
QnA Maker
Enables the real-time transcription of speech-to-text.
Speech
This question tests understanding of Azure Cognitive Services and their use cases as outlined in the Microsoft Azure AI Fundamentals (AI-900) study guide.
* "Enables the use of natural language to query a knowledge base." # QnA MakerAccording to Microsoft Learn's AI-900 module "Identify features of Natural Language Processing (NLP) workloads and services," QnA Maker is a cloud-based service that allows developers to build a question-and-answer layer over structured or unstructured content. It enables users to ask questions in natural language, and the service retrieves the most relevant answer from a knowledge base (such as FAQs, manuals, or documents).QnA Maker uses Natural Language Processing (NLP) techniques to interpret user intent and return an appropriate response. It is often integrated into chatbots built with Azure Bot Service to make them capable of conversational question-answering. In the newer Azure Cognitive Services lineup, QnA Maker capabilities are merged into Azure Cognitive Service for Language (Question Answering).
* "Enables the real-time transcription of speech-to-text." # SpeechThe Azure Speech service (part of Azure Cognitive Services) provides the ability to convert spoken language into written text in real time.
This feature, called Speech-to-Text, uses deep neural network models to recognize and transcribe human speech with high accuracy.Microsoft's AI-900 documentation specifies that Speech service capabilities also include text-to-speech, speech translation, and speaker recognition. Real-time transcription is widely used in applications such as voice assistants, captioning systems, call analytics, and accessibility tools.
Other listed services such as Azure Storage and Language Understanding (LUIS) serve different purposes:
* Azure Storage handles data storage, not AI workloads.
* LUIS identifies user intent from natural language but does not query knowledge bases directly.

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