Anomaly detection MCQs January 8, 2026November 18, 2024 by u930973931_answers 10 min Score: 0 Attempted: 0/10 Subscribe 1. What is the primary goal of anomaly detection in machine learning? (A) To classify data into predefined categories (B) To minimize the error in predictions (C) To predict future data points (D) To identify rare patterns that do not conform to expected behavior 2. Which of the following is NOT a typical application of anomaly detection? (A) Fraud detection (B) Stock price prediction (C) Network security monitoring (D) Image outlier detection 3. Which of these methods is commonly used for anomaly detection in high-dimensional data? (A) Principal Component Analysis (PCA) (B) K-Nearest Neighbors (K-NN) (C) Linear Regression (D) Decision Trees 4. In the context of anomaly detection, what is a “point anomaly”? (A) An anomaly that deviates significantly from the general distribution of the data (B) An anomaly that affects a group of data points (C) An anomaly that occurs in a sequence of data points (D) An anomaly detected using clustering methods 5. Which of the following techniques is unsupervised in nature for anomaly detection? (A) K-Means Clustering (B) Decision Trees (C) Support Vector Machines (SVM) (D) Naive Bayes 6. The Isolation Forest algorithm is primarily used for: (A) Classification problems (B) Anomaly detection in high-dimensional datasets (C) Regression problems (D) Dimensionality reduction 7. In anomaly detection, which of the following metrics is typically used to evaluate the performance of an algorithm? (A) Accuracy (B) Precision and Recall (C) Mean Squared Error (MSE) (D) F1 Score 8. Which of the following statements about local outlier factor (LOF) is true? (A) LOF detects outliers by measuring the density of a data point compared to its neighbors (B) LOF is a supervised learning algorithm (C) LOF is not suitable for high-dimensional data (D) LOF requires labeled data for training 9. Which of the following is a common challenge when performing anomaly detection in time-series data? (A) Identifying the data’s correct labeling (B) Handling missing data (C) Choosing the right distance metric for clustering (D) Detecting anomalies in non-stationary data 10. What is the primary assumption in One-Class SVM when detecting anomalies? (A) Data points are evenly distributed across all classes (B) The data is always Gaussian-distributed (C) Anomalies are labeled as separate classes during training (D) Data points from the normal class are close to each other and form a tight cluster