Microsoft Azure AI Fundamentals (AI-900 Korean Version) - AI-900 Korean Exam Practice Test

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."

Explanation:

The Azure OpenAI Service provides access to advanced Generative Pre-trained Transformer (GPT) models developed by OpenAI, such as GPT-3, GPT-3.5, and GPT-4. These models are capable of performing a wide range of natural language processing (NLP) and generative AI tasks - including text completion, summarization, translation, question answering, content creation, and code generation.
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI, this managed service allows developers to deploy and integrate GPT-based models within their own applications using REST APIs or the Azure SDK. The service handles scalability, performance, and infrastructure automatically, meaning users do not need to manage servers or computational resources manually.
Option review:
* Supports the deployment of GPT-based models - # Correct. Azure OpenAI is specifically designed for deploying and operationalizing GPT models and similar transformer-based architectures.
* Provides capabilities exclusively for vision-related tasks - # Incorrect. Vision tasks (like image classification or object detection) are part of Azure AI Vision.
* Provides capabilities exclusively for speech-related tasks - # Incorrect. Speech processing (speech-to- text, text-to-speech, translation) belongs to Azure AI Speech Services, not Azure OpenAI.
* Requires manual infrastructure management for scalability - # Incorrect. Azure OpenAI is a fully managed service; scalability and performance are handled automatically by Azure.
Therefore, the correct completion of the sentence is:
"Azure OpenAI Service supports the deployment of GPT-based models."

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation:

Reliability and safety: To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles AI systems should perform reliably and safely. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of system can result in substantial risk to human life.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation:

This question is based on identifying Natural Language Processing (NLP) workloads, which is a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) certification. According to the official Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure", NLP enables computers to understand, interpret, and generate human language - both written and spoken.
* A bot that responds to queries by internal users - YesThis is an example of a natural language processing workload because it involves understanding and generating human language. A chatbot interprets user input (queries written or spoken) using language understanding and text analytics, and then produces appropriate responses. On Azure, this can be implemented using Azure AI Language (LUIS) and the Azure Bot Service, both core NLP technologies.
* A mobile application that displays images relating to an entered search term - NoThis application involves searching for or displaying images, which falls under the computer vision workload, not NLP.
Computer vision focuses on analyzing and interpreting visual data like photos or videos, while NLP deals with language and text processing.
* A web form used to submit a request to reset a password - NoA password reset form involves structured input fields and user authentication, not natural language understanding or generation. It's part of standard web development and identity management, not an NLP-related process.
Therefore, based on Microsoft's AI-900 curriculum definitions:
# The only true NLP example is the bot responding to user queries, since it processes and understands natural language input to generate conversational output.

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Explanation:

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure