Free Microsoft AI-900 Korean Practice Test & Real Exam Questions

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," a regression model is used when the goal is to predict a continuous numerical value based on historical data.
In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.
Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.
Let's review the incorrect options:
* Classification: Used to predict discrete categories or labels, such as "sold" vs. "unsold" or "low,"
"medium," "high." It cannot output continuous numeric predictions.
* Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.
Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.
This aligns with Microsoft's AI-900 teaching that regression is used for tasks such as:
* Predicting house prices
* Forecasting sales revenue
* Estimating car values or auction prices

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation:

The correct order of processes before deploying a model as a service is:
(1) Data preparation # (2) Model training # (3) Model evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore the machine learning process", machine learning follows a structured lifecycle that involves several sequential stages. Before a model can be deployed, the data must be properly prepared, the model must be trained, and then its performance must be evaluated to ensure accuracy and reliability.
* Data Preparation:The first stage involves collecting, cleaning, and transforming raw data into a usable format. Azure Machine Learning provides tools like Data Wrangler, Data Labeling, and Data Transformation pipelines to ensure the dataset is accurate and consistent. As per Microsoft Learn, "data preparation is essential to remove noise, handle missing values, and split the dataset into training and testing sets." This step ensures the model learns from quality input.
* Model Training:In this step, algorithms are applied to the prepared training data to create a predictive model. The system learns patterns and relationships from the data. Azure Machine Learning allows model training using AutoML, custom code, or designer pipelines. The training process produces a model that can make predictions, but it still needs to be tested before deployment.
* Model Evaluation:Once trained, the model's performance is tested against unseen (test) data.
Evaluation metrics like accuracy, precision, recall, and F1-score are analyzed to verify if the model meets business and performance requirements. Microsoft Learn defines this stage as "assessing the model's performance to determine its readiness for deployment." After these three processes, the model can then be deployed as a web service using Azure Machine Learning endpoints. Model retraining happens later when new data becomes available, and data encryption is a security process, not part of model development steps.

Explanation:

Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to " fit " your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features

Explanation:

Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:

The task is to build a machine learning model in Azure Machine Learning designer to predict automobile prices, which is a regression problem since the output (price) is a continuous numeric value. The pipeline must follow the logical data preparation, training, and evaluation flow as outlined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module "Create a machine learning model with Azure Machine Learning designer." Here's the correct sequence and reasoning:
* Select Columns in Dataset:The first step after loading the raw automobile dataset is to choose the relevant columns that will be used as features (inputs) and the label (output). This module ensures that only necessary fields (for example, horsepower, engine size, mileage, etc.) are used to train the model while excluding irrelevant columns like vehicle ID or serial number.
* Split Data:Next, the cleaned and filtered dataset must be split into two subsets: training data and testing data (often 70/30 or 80/20). This allows the model to be trained on one portion and evaluated on the other to measure predictive accuracy.
* Linear Regression:Since automobile price prediction is a numeric prediction task, the appropriate learning algorithm is Linear Regression. This supervised algorithm learns relationships between numeric features and the target (price).
Finally, the workflow connects the training data and Linear Regression module to the Train Model module, which outputs a trained regression model. The trained model is then linked to the Score Model module to compare predicted vs. actual prices.
This pipeline fully aligns with Microsoft's recommended process for regression in Azure ML Designer.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of conversational AI workloads," the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.
Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform's interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.
This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft's language understanding models, allowing it to interpret user input and map it to predefined topics or actions.
Let's analyze the other options:
* Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.
* Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.
Therefore, the most appropriate choice that "can be used to build no-code apps that use built-in natural language processing models" is Power Virtual Agents - the official Microsoft no-code chatbot solution for conversational AI workloads.

Explanation:
# Yes - Extract key phrases
# No - Generate press releases
# Yes - Detect sentiment
The Azure AI Language service is a powerful set of natural language processing (NLP) tools within Azure Cognitive Services, designed to analyze, understand, and interpret human language in text form. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, this service includes several capabilities such as key phrase extraction, sentiment analysis, language detection, named entity recognition (NER), and question answering.
* Extract key phrases from documents # YesThe Key Phrase Extraction feature identifies the most relevant words or short phrases within a document, helping summarize important topics. This is useful for indexing, summarizing, or organizing content. For instance, from "Azure AI Language helps analyze customer feedback," it may extract "Azure AI Language" and "customer feedback" as key phrases.
* Generate press releases based on user prompts # NoThis functionality falls under generative AI, specifically within Azure OpenAI Service, which uses models such as GPT-4 for text creation. The Azure AI Language service focuses on analyzing and understanding existing text, not generating new content like press releases or articles.
* Build a social media feed analyzer to detect sentiment # YesThe Sentiment Analysis capability determines the emotional tone (positive, neutral, negative, or mixed) of text data, making it ideal for analyzing social media posts, reviews, or feedback. Businesses often use this to gauge customer satisfaction or brand reputation.
In summary, the Azure AI Language service analyzes text to extract insights and detect sentiment but does not generate new textual content.

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).