AI-102 Exam Questions: What Are Two Types of Supervised Machine Learning Algorithms? (Choose Two.)

Microsoft AI-102 Certification Exam: A Detailed Overview

The Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution Certification is a critical credential for AI professionals, validating expertise in building, deploying, and managing AI solutions on Azure. A key exam question, “What are two types of supervised machine learning algorithms? (Choose two.),” highlights classification and regression algorithms, tested within Domain 2: Implement Computer Vision Solutions (20–25%) and Domain 3: Implement Natural Language Processing Solutions (20–25%). These domains cover machine learning fundamentals, Azure AI services, and model deployment, essential for roles like AI engineers, data scientists, and solutions architects.

The AI-102 exam, lasting 120 minutes with 40–60 questions, includes multiple-choice, case studies, and lab-based questions, requiring a passing score of approximately 700 (on a 100–1000 scale). Study4Pass is a premier resource for AI-102 preparation, offering comprehensive study guides, practice exams, and hands-on labs tailored to the exam syllabus. This article explores supervised machine learning, classification and regression algorithms, their Azure applications, and strategic preparation tips using Study4Pass to excel in the Microsoft AI-102 certification exam.

Introduction to Supervised Machine Learning

Definition & Core Concept

Supervised machine learning is a subset of machine learning where models are trained on labeled datasets data with input-output pairs to predict outcomes for new, unseen data. The “supervision” comes from the labeled outputs, guiding the model to learn relationships between inputs (features) and outputs (labels).

Core Mechanics:

• Training: The model learns from a dataset with known inputs (e.g., customer demographics) and outputs (e.g., purchase likelihood).
• Prediction: The trained model predicts outputs for new inputs.
• Evaluation: Metrics like accuracy (for classification) or mean squared error (for regression) assess performance.

Example: Predicting house prices (output: price) based on size and location (inputs) using a labeled dataset of past sales.

For AI-102 candidates, understanding supervised learning is foundational, as it underpins Azure AI services like Azure Machine Learning. Study4Pass provides detailed guides on supervised learning, supported by practice questions that reinforce core concepts.

Relevance to Microsoft AI-102 Exam

The AI-102 exam tests supervised learning in objectives like “Select the appropriate machine learning algorithm” and “Train and deploy models using Azure Machine Learning.” Candidates must:

• Identify supervised learning types (classification, regression).
• Select algorithms for Azure AI tasks (e.g., image classification, demand forecasting).
• Configure and evaluate models in Azure Machine Learning Studio.

Exam questions may involve choosing algorithm types, configuring Azure pipelines, or analyzing model outputs. Study4Pass aligns its resources with these objectives, offering labs and practice exams that mirror real-world Azure AI scenarios.

Two Primary Types of Supervised Learning (Exam Focus)

The AI-102 exam question asks for two types of supervised machine learning algorithms. The answers are:

Classification Algorithms

• Definition: Predicts discrete categories or classes (e.g., “yes/no,” “spam/not spam”).
• Key Features:
  o    Outputs are categorical (e.g., binary or multiclass).
  o    Uses metrics like accuracy, precision, recall, and F1-score.
• Common Algorithms:
  o    Logistic Regression: Predicts probabilities for binary outcomes (e.g., customer churn).
  o    Decision Trees: Splits data into branches for classification (e.g., fraud detection).
  o    Support Vector Machines (SVM): Finds optimal boundaries between classes (e.g., image classification).
  o    Random Forest: Combines multiple trees for robust predictions (e.g., sentiment analysis).
• Example: Classifying emails as “spam” or “not spam” based on keywords and sender data.
• Azure Use Case: Azure Machine Learning’s classification module for predicting customer churn.

Regression Algorithms

• Definition: Predicts continuous numerical values (e.g., house prices, temperature).
• Key Features:
  o    Outputs are numerical and unbounded.
  o    Uses metrics like mean squared error (MSE), mean absolute error (MAE), and R².
• Common Algorithms:
  o    Linear Regression: Fits a line to predict values (e.g., sales forecasting).
  o    Ridge/Lasso Regression: Adds regularization to prevent overfitting (e.g., stock price prediction).
  o    Decision Trees for Regression: Predicts numerical outcomes (e.g., energy consumption).
  o    Gradient Boosting (e.g., XGBoost): Iteratively improves predictions (e.g., demand forecasting).
• Example: Predicting a house’s price based on size, location, and bedrooms.
• Azure Use Case: Azure Machine Learning’s regression module for forecasting inventory needs.

For AI-102 candidates, distinguishing these types is critical. Study4Pass flashcards highlight classification vs. regression, ensuring quick recall.

Comparison: Classification vs. Regression

Key Insight: Classification answers “what category?” while regression answers “how much?” For AI-102, understanding this distinction is key, as exam questions may test algorithm selection. Study4Pass guides include comparison tables, supported by practice questions on algorithm applications.

Microsoft AI-102 Exam Focus Areas

How Questions Are Framed

• Multiple-Choice: “What are two types of supervised machine learning algorithms? (Choose two.)” (Answer: Classification, Regression).
• Scenario-Based: Select an algorithm for a task (e.g., regression for price prediction).
• Lab-Based: Configure a classification model in Azure Machine Learning Studio.
• Example: “Which algorithm predicts customer satisfaction scores (1–5)?” (Answer: Classification).
• Study4Pass Tip: Practice 50 algorithm-focused questions.

Azure Machine Learning Studio Integration

• Role: Azure ML Studio simplifies model training, evaluation, and deployment.
• Features:
  o    Designer: Drag-and-drop pipelines for classification/regression models.
  o    AutoML: Automatically selects algorithms (e.g., Random Forest, Linear Regression).
  o    Metrics: Visualizes accuracy (classification) or MSE (regression).
• Example: Use Azure ML Designer to train a logistic regression model for sentiment analysis.
• 212-89 Relevance: Questions may test pipeline configuration or metric interpretation.

Study4Pass labs provide virtual Azure ML environments, ensuring hands-on proficiency.

Practical Applications in Azure AI

Classification Example

• Scenario: A retailer wants to predict customer churn (yes/no).
• Azure Solution:
  I.      Data: Upload customer data (age, purchase history, complaints) to Azure ML.
  II.      Model: Train a Random Forest classifier in Azure ML Designer.
  III.      Evaluation: Achieve 85% accuracy, 80% precision.
  IV.      Deployment: Deploy as a web service for real-time predictions.
• Outcome: Identified at-risk customers, enabling targeted retention campaigns.
• AI-102 Relevance: Questions may involve configuring classification pipelines.

Regression Example

• Scenario: A manufacturer needs to forecast monthly inventory demand.
• Azure Solution:
  I.      Data: Import historical sales data to Azure ML.
  II.      Model: Train a Gradient Boosting regressor in Azure ML Designer.
  III.      Evaluation: Achieve MSE of 150 units, R² of 0.92.
  IV.      Deployment: Integrate with Azure Functions for automated forecasting.
• Outcome: Optimized inventory levels, reducing overstock costs.
• AI-102 Relevance: Questions may test regression model evaluation.

Study4Pass labs simulate these applications, reinforcing Azure AI skills.

Study Tips for AI-102 Exam

Memory Aids

• Mnemonic: “C-R” (Classification: Categories, Regression: Real numbers).
• Visualization: Picture classification as sorting items into bins, regression as plotting a line.
• Study4Pass Tip: Use flashcards for algorithm types and metrics.

Hands-On Practice

1. Lab 1: Classification Pipeline:
   o    Build a logistic regression model in Azure ML for spam detection.
   o    Outcome: Mastered classification workflows.
2. Lab 2: Regression Pipeline:
   o    Train a linear regression model in Azure ML for price prediction.
   o    Outcome: Understood regression metrics.
3. Lab 3: AutoML:
   o    Use Azure ML AutoML to compare classification and regression models.
   o    Outcome: Learned automated algorithm selection.

• Tool: Study4Pass Azure ML labs.

Practice Questions

1. Question: What are two types of supervised machine learning algorithms? (Choose two.)
   o    A. Clustering
   o    B. Classification
   o    C. Regression
   o    D. Dimensionality Reduction
   o    Answer: B, C
   o    Explanation: Classification and regression are supervised; clustering and dimensionality reduction are unsupervised.
2. Question: Which metric evaluates a regression model?
   o    Answer: Mean Squared Error (MSE)
   o    Explanation: MSE measures numerical prediction errors, unlike accuracy (classification).

Study Plan

• Weeks 1–2: Memorize classification/regression definitions, algorithms, metrics.
• Weeks 3–4: Practice Azure ML labs (classification, regression, AutoML).
• Weeks 5–6: Solve 100-question practice tests, focus on scenarios.
• Study4Pass Tip: Join forums for peer support on Azure AI questions.

Beyond the Basics: Advanced Considerations

Ensemble Methods (Combining Models)

• Definition: Combine multiple models to improve performance (e.g., Random Forest, Gradient Boosting).
• Classification: Random Forest averages decision tree predictions for robust spam detection.
• Regression: XGBoost boosts weak learners for accurate demand forecasting.
• Azure Support: Azure ML supports ensemble models via Designer and Python SDK.
• AI-102 Relevance: Questions may test ensemble algorithm selection.

Deep Learning for Supervised Tasks

• Definition: Neural networks for complex supervised tasks (e.g., image classification, time-series forecasting).
• Classification: Convolutional Neural Networks (CNNs) for Azure Custom Vision.
• Regression: Recurrent Neural Networks (RNNs) for stock price prediction.
• Azure Support: Azure ML integrates with TensorFlow/PyTorch for deep learning.
• AI-102 Relevance: Questions may involve deep learning model deployment.

Model Interpretability in Azure

• Definition: Techniques to explain model predictions (e.g., SHAP, LIME).
• Importance: Ensures trust and compliance in AI solutions.
• Azure Support: Azure ML’s Explainability Toolkit visualizes feature importance.
• Example: SHAP shows age as the top predictor in a churn model.
• AI-102 Relevance: Questions may test interpretability tools.

Study4Pass guides cover these advanced topics, preparing candidates for complex questions.

Final Verdict

The Microsoft AI-102 certification equips AI professionals with cutting-edge skills, with supervised machine learning algorithms—classification (predicting categories) and regression (predicting numbers)—as critical topics in Computer Vision and Natural Language Processing domains. These algorithms power Azure AI solutions, from churn prediction to demand forecasting, and mastering their selection, configuration, and deployment is key to exam success and real-world proficiency.

Study4Pass is the ultimate resource for AI-102 preparation, offering study guides, practice exams, and hands-on labs that replicate real-world Azure AI scenarios. Its algorithm-focused labs and scenario-based questions ensure candidates can build models, evaluate metrics, and deploy solutions confidently. With Study4Pass, aspiring AI-102 professionals can ace the exam and launch rewarding careers, with salaries averaging $100,000–$150,000 annually (Glassdoor, 2025).

Exam Tips:

• Memorize classification and regression for multiple-choice questions.
• Practice Azure ML pipelines in Study4Pass labs for lab-based tasks.
• Solve scenarios to select algorithms for specific tasks.
• Review ensemble methods and interpretability for advanced questions.
• Complete timed practice tests to manage the 40–60-question, 120-minute exam efficiently.

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Practice Questions from Microsoft AI-102 Certification Exam

What are two types of supervised machine learning algorithms? (Choose two.)

A. Clustering
B. Classification
C. Regression
D. Association

A company wants to predict house prices based on size and location. Which supervised machine learning algorithm type should be used?

A. Classification
B. Regression
C. Clustering
D. Dimensionality Reduction

Which Azure Machine Learning feature automatically selects the best classification algorithm for a dataset?

A. Designer
B. AutoML
C. Python SDK
D. Explainability Toolkit

Which metric is used to evaluate a classification model in Azure Machine Learning?

A. Mean Squared Error
B. R²
C. Accuracy
D. Mean Absolute Error

A developer trains a model in Azure Machine Learning to classify images as “cat” or “dog.” Which algorithm is suitable?

A. Linear Regression
B. Random Forest
C. K-Means Clustering
D. Principal Component Analysis