{
  "title": "DBSCAN Mastery: 100 MCQs",
  "description": "A comprehensive set of 100 multiple-choice questions designed to test and deepen your understanding of DBSCAN (Density-Based Spatial Clustering of Applications with Noise), covering fundamental concepts, parameters, advantages, limitations, and practical scenarios.",
  "questions": [
    {
      "id": 1,
      "questionText": "What is the main idea behind DBSCAN clustering?",
      "options": [
        "Clusters are dense regions separated by sparse regions",
        "All points are assigned to a cluster",
        "Clusters are linearly separable",
        "Clusters are formed by equal-sized groups"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN identifies clusters based on density: areas with many points form clusters, and sparse regions separate them."
    },
    {
      "id": 2,
      "questionText": "DBSCAN requires which key parameters?",
      "options": [
        "Number of clusters (k) only",
        "Learning rate and iterations",
        "Distance metric only",
        "Epsilon (eps) and Minimum points (minPts)"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN uses eps (neighborhood radius) and minPts (minimum points to form a dense region) to define clusters."
    },
    {
      "id": 3,
      "questionText": "In DBSCAN, what is a 'core point'?",
      "options": [
        "Point on the boundary of clusters",
        "Point with no neighbors",
        "Point with at least minPts neighbors within eps",
        "Any point in the dataset"
      ],
      "correctAnswerIndex": 2,
      "explanation": "A core point has enough neighboring points within eps to be considered part of a dense cluster."
    },
    {
      "id": 4,
      "questionText": "In DBSCAN, what is a 'border point'?",
      "options": [
        "Point not in any cluster",
        "Point reachable from a core point but with fewer than minPts neighbors",
        "Point with more than minPts neighbors",
        "Centroid of a cluster"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Border points are density-reachable from core points but do not have enough neighbors themselves to be core points."
    },
    {
      "id": 5,
      "questionText": "In DBSCAN, what is a 'noise point'?",
      "options": [
        "Point with maximum density",
        "Point on the cluster centroid",
        "Point with exactly minPts neighbors",
        "Point not reachable from any core point"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Noise points are isolated points that do not belong to any cluster."
    },
    {
      "id": 6,
      "questionText": "Scenario: You have clusters of varying density. Challenge for DBSCAN?",
      "options": [
        "DBSCAN fails to run",
        "Always finds all clusters perfectly",
        "Clusters become linearly separable",
        "May merge dense clusters and miss sparse ones"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN struggles with clusters of differing densities because eps and minPts are global parameters."
    },
    {
      "id": 7,
      "questionText": "Scenario: Choosing eps too large. Effect?",
      "options": [
        "Algorithm fails",
        "Noise increases",
        "More clusters detected",
        "Clusters may merge; noise reduced"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Large eps connects distant points, possibly merging distinct clusters."
    },
    {
      "id": 8,
      "questionText": "Scenario: Choosing eps too small. Effect?",
      "options": [
        "Clusters merge",
        "Many points labeled as noise; clusters fragmented",
        "No effect",
        "EM applied instead"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Small eps results in fewer neighbors; many points cannot form clusters and are marked as noise."
    },
    {
      "id": 9,
      "questionText": "Scenario: Setting minPts too high. Effect?",
      "options": [
        "Clusters merge",
        "More points labeled as noise; small clusters ignored",
        "Algorithm fails",
        "Clusters increase"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High minPts requires dense regions to form clusters, excluding smaller or sparse clusters."
    },
    {
      "id": 10,
      "questionText": "Scenario: Setting minPts too low. Effect?",
      "options": [
        "DBSCAN fails",
        "Clusters disappear",
        "Many small clusters; noise reduced",
        "Clusters merge automatically"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Low minPts allows small groups to form clusters, potentially splitting natural clusters."
    },
    {
      "id": 11,
      "questionText": "Scenario: A border point is connected to multiple core points of different clusters. How is it assigned?",
      "options": [
        "Becomes noise automatically",
        "Forms a new cluster",
        "Assigned to any one cluster arbitrarily or first reachable",
        "Algorithm fails"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Border points can belong to one cluster; usually assigned to the first core point that reaches it."
    },
    {
      "id": 12,
      "questionText": "Scenario: You have 2D spatial data with noise. DBSCAN advantage?",
      "options": [
        "Detects clusters of arbitrary shape and identifies noise",
        "Requires clusters to be circular",
        "Sensitive to number of clusters parameter",
        "Assigns all points to clusters"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN works well for arbitrary shapes and identifies noise points."
    },
    {
      "id": 13,
      "questionText": "Scenario: Using Euclidean distance vs Manhattan distance in DBSCAN. Effect?",
      "options": [
        "Distance metric affects cluster shapes and eps choice",
        "DBSCAN fails",
        "No effect; clusters same",
        "Noise ignored"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Different distance metrics affect neighborhood calculation, which can change clustering."
    },
    {
      "id": 14,
      "questionText": "Scenario: DBSCAN applied on high-dimensional data. Challenge?",
      "options": [
        "Distance measures become less meaningful (curse of dimensionality)",
        "Algorithm runs faster",
        "Clusters automatically reduce",
        "Noise decreases"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High dimensions can make points appear equidistant, complicating density estimation."
    },
    {
      "id": 15,
      "questionText": "Scenario: You have concentric clusters. DBSCAN challenge?",
      "options": [
        "May fail to separate inner and outer clusters depending on eps",
        "Always separates perfectly",
        "Clusters merge automatically",
        "Noise increases"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Density difference between inner and outer rings may cause DBSCAN to merge or mislabel clusters."
    },
    {
      "id": 16,
      "questionText": "Scenario: Using DBSCAN for geospatial clustering. Advantage?",
      "options": [
        "Finds clusters of arbitrary shape like regions or neighborhoods",
        "Clusters must be circular",
        "All points assigned",
        "Sensitive to number of clusters"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN can identify irregularly shaped spatial clusters without specifying cluster count."
    },
    {
      "id": 17,
      "questionText": "Scenario: You want clusters of varying density. DBSCAN limitation?",
      "options": [
        "Noise removed automatically",
        "Single global eps may not detect all clusters",
        "Algorithm adapts automatically",
        "All clusters found"
      ],
      "correctAnswerIndex": 1,
      "explanation": "DBSCAN uses a fixed eps, which can miss sparse clusters or merge dense clusters."
    },
    {
      "id": 18,
      "questionText": "Scenario: You apply DBSCAN on streaming data. Challenge?",
      "options": [
        "Noise ignored",
        "Automatically updates clusters",
        "All points reassigned automatically",
        "Standard DBSCAN is static; streaming adaptation required"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN is not incremental; modifications are needed for dynamic/streaming data."
    },
    {
      "id": 19,
      "questionText": "Scenario: Using DBSCAN for anomaly detection. Approach?",
      "options": [
        "Assign random labels",
        "Label points not in any cluster as anomalies",
        "Clusters merged manually",
        "Use all clusters for prediction"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Noise points are naturally flagged as outliers."
    },
    {
      "id": 20,
      "questionText": "Scenario: DBSCAN vs K-Means on arbitrary-shaped clusters. Advantage?",
      "options": [
        "DBSCAN fails for shapes",
        "Both perform equally",
        "K-Means better for arbitrary shapes",
        "DBSCAN can capture non-spherical clusters; K-Means cannot"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN works with clusters of any shape without requiring centroids."
    },
    {
      "id": 21,
      "questionText": "Scenario: Two clusters are close together but separated by sparse points. DBSCAN outcome?",
      "options": [
        "Fails to converge",
        "Marks everything as noise",
        "Correctly separates clusters using density differences",
        "Merges clusters automatically"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sparse points allow DBSCAN to distinguish dense clusters even if they are close."
    },
    {
      "id": 22,
      "questionText": "Scenario: Applying DBSCAN on 3D point cloud data. Advantage?",
      "options": [
        "Clusters must be spherical",
        "All points assigned to clusters",
        "Can find clusters of arbitrary 3D shape and ignore noise",
        "Requires predefining cluster centers"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN handles multi-dimensional data and can identify irregular clusters and noise."
    },
    {
      "id": 23,
      "questionText": "Scenario: DBSCAN uses Manhattan distance on grid data. Effect?",
      "options": [
        "Algorithm fails",
        "Noise increases automatically",
        "Clusters align with grid; eps choice differs",
        "No effect on clusters"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Distance metric changes the neighborhood definition, affecting cluster formation."
    },
    {
      "id": 24,
      "questionText": "Scenario: You want small but dense clusters. How to set parameters?",
      "options": [
        "Small eps and appropriate minPts",
        "Large eps",
        "Ignore parameters",
        "Large minPts"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Smaller eps ensures that small dense regions form separate clusters."
    },
    {
      "id": 25,
      "questionText": "Scenario: You have noisy sensor data. DBSCAN benefit?",
      "options": [
        "Clusters all points",
        "Fails with noise",
        "Requires K-Means preprocessing",
        "Automatically labels isolated points as noise"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN identifies low-density points as noise, avoiding misclassification."
    },
    {
      "id": 26,
      "questionText": "Scenario: Data with hierarchical cluster structure. Limitation of DBSCAN?",
      "options": [
        "Noise ignored",
        "All clusters merged",
        "Cannot detect hierarchy; only flat clusters",
        "Automatically finds hierarchy"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN provides flat clustering; hierarchical relationships are not captured."
    },
    {
      "id": 27,
      "questionText": "Scenario: Using DBSCAN for image segmentation. Advantage?",
      "options": [
        "Requires predefined number of segments",
        "All pixels assigned to clusters",
        "Identifies irregular regions and isolates noise",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN captures arbitrary-shaped regions and treats background/noisy pixels as noise."
    },
    {
      "id": 28,
      "questionText": "Scenario: You have clusters of different densities. How to adapt DBSCAN?",
      "options": [
        "Reduce dimensionality",
        "Use varying eps with methods like HDBSCAN",
        "Keep single global eps",
        "Increase minPts"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Standard DBSCAN struggles with varying densities; adaptive versions like HDBSCAN help."
    },
    {
      "id": 29,
      "questionText": "Scenario: DBSCAN fails to detect clusters in high-dimensional text embeddings. Solution?",
      "options": [
        "Increase minPts arbitrarily",
        "Use full covariance",
        "Reduce dimensions using PCA or t-SNE before clustering",
        "Ignore scaling"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Dimensionality reduction improves distance computation and density estimation."
    },
    {
      "id": 30,
      "questionText": "Scenario: Choosing minPts in DBSCAN. Rule of thumb?",
      "options": [
        "MinPts = 1 always",
        "MinPts = dataset size",
        "MinPts ignored",
        "MinPts ≥ dimensionality + 1"
      ],
      "correctAnswerIndex": 3,
      "explanation": "MinPts should be slightly larger than the data dimensionality for meaningful clusters."
    },
    {
      "id": 31,
      "questionText": "Scenario: DBSCAN applied on GPS data of taxis in a city. Best use case?",
      "options": [
        "Assign random clusters",
        "Identify high-density pickup/drop-off hotspots",
        "Detect only circular areas",
        "Cluster by taxi color"
      ],
      "correctAnswerIndex": 1,
      "explanation": "DBSCAN can detect dense regions where taxis frequently gather without assuming cluster shape."
    },
    {
      "id": 32,
      "questionText": "Scenario: You notice DBSCAN marks too many points as noise. Likely cause?",
      "options": [
        "Algorithm failed",
        "All clusters are too dense",
        "Distance metric wrong",
        "eps too small or minPts too high"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Small eps or high minPts can make points unable to form clusters, labeling them as noise."
    },
    {
      "id": 33,
      "questionText": "Scenario: DBSCAN applied to social network graph. Challenge?",
      "options": [
        "Clusters are always detected",
        "DBSCAN works directly on graph",
        "Noise ignored",
        "Graph edges may not correspond to meaningful distances; need transformation"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN requires distance metrics; graphs need embedding or distance conversion."
    },
    {
      "id": 34,
      "questionText": "Scenario: Using DBSCAN for anomaly detection in network traffic. How?",
      "options": [
        "Label low-density patterns as anomalies",
        "All high-traffic nodes flagged",
        "Randomly assign anomalies",
        "Clusters merged manually"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Low-density points correspond to unusual patterns, suitable for anomaly detection."
    },
    {
      "id": 35,
      "questionText": "Scenario: High-dimensional DBSCAN performance issue. Solution?",
      "options": [
        "Use dimensionality reduction or HDBSCAN",
        "Ignore distance metric",
        "Use K-Means instead",
        "Increase eps arbitrarily"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Reducing dimensions or using hierarchical density clustering helps in high-dimensional spaces."
    },
    {
      "id": 36,
      "questionText": "Scenario: Clusters are elongated. DBSCAN vs K-Means?",
      "options": [
        "K-Means works better",
        "All points assigned to noise",
        "DBSCAN captures arbitrary shapes better",
        "Both fail"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN does not assume spherical clusters, so elongated shapes are captured well."
    },
    {
      "id": 37,
      "questionText": "Scenario: DBSCAN fails on variable-density clusters. Solution?",
      "options": [
        "Reduce minPts to 1",
        "Use HDBSCAN for adaptive density clustering",
        "Increase eps globally",
        "Ignore problem"
      ],
      "correctAnswerIndex": 1,
      "explanation": "HDBSCAN handles clusters with varying density better than standard DBSCAN."
    },
    {
      "id": 38,
      "questionText": "Scenario: You want reproducible DBSCAN results. Requirement?",
      "options": [
        "Ignore minPts",
        "Deterministic neighbor search and consistent distance metric",
        "Random initialization",
        "Vary eps each run"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Reproducibility requires deterministic calculations for neighborhoods and distances."
    },
    {
      "id": 39,
      "questionText": "Scenario: DBSCAN applied on time-series sensor readings. Approach?",
      "options": [
        "Use sliding windows to extract features before clustering",
        "Clusters automatically detected",
        "Apply DBSCAN on raw timestamps",
        "Ignore feature extraction"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Time-series features are extracted to represent temporal patterns for density-based clustering."
    },
    {
      "id": 40,
      "questionText": "Scenario: You need clusters and hierarchy. Limitation of DBSCAN?",
      "options": [
        "Noise ignored",
        "DBSCAN provides only flat clustering",
        "Automatically generates hierarchy",
        "Clusters nested by default"
      ],
      "correctAnswerIndex": 1,
      "explanation": "DBSCAN produces flat clusters; hierarchical relationships require extensions like HDBSCAN."
    },
    {
      "id": 41,
      "questionText": "Scenario: DBSCAN applied on customer purchase patterns. Advantage?",
      "options": [
        "Requires predefined cluster number",
        "Sensitive to initial seed",
        "Detects dense buying behavior groups and isolates rare patterns",
        "All points assigned"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN identifies dense purchasing patterns and separates anomalies naturally."
    },
    {
      "id": 42,
      "questionText": "Scenario: You want to tune DBSCAN eps parameter. Approach?",
      "options": [
        "MinPts adjustment only",
        "Always choose maximum distance",
        "Use k-distance graph to identify elbow point",
        "Randomly guess eps"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Plotting k-distance helps find a suitable eps where distances start increasing sharply."
    },
    {
      "id": 43,
      "questionText": "Scenario: DBSCAN with overlapping clusters. Effect?",
      "options": [
        "Points duplicated",
        "Clusters fail completely",
        "Overlap handled by density; border points assigned to one cluster",
        "Noise ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN assigns border points to a reachable cluster; soft assignment is not available."
    },
    {
      "id": 44,
      "questionText": "Scenario: Applying DBSCAN to text embeddings. Challenge?",
      "options": [
        "Noise ignored",
        "All points assigned to clusters",
        "DBSCAN always works",
        "High-dimensional distances may be less meaningful"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Distance measures in high dimensions can reduce effectiveness; dimensionality reduction helps."
    },
    {
      "id": 45,
      "questionText": "Scenario: Noise proportion is high. DBSCAN behavior?",
      "options": [
        "Many points labeled as noise; cluster detection limited",
        "Algorithm fails",
        "Clusters detected perfectly",
        "All points assigned to clusters"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High noise density can prevent formation of dense clusters."
    },
    {
      "id": 46,
      "questionText": "Scenario: DBSCAN on streaming data. Limitation?",
      "options": [
        "Standard DBSCAN is static; needs incremental adaptation",
        "All points reassigned automatically",
        "Noise ignored",
        "Automatically updates clusters"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN is not inherently incremental; streaming data requires modified algorithms."
    },
    {
      "id": 47,
      "questionText": "Scenario: DBSCAN vs K-Means for non-spherical clusters. Advantage?",
      "options": [
        "Both fail",
        "K-Means better",
        "Noise ignored",
        "DBSCAN detects arbitrary shapes; K-Means cannot"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN does not rely on centroid or spherical assumption."
    },
    {
      "id": 48,
      "questionText": "Scenario: You apply DBSCAN on noisy sensor readings. Outcome?",
      "options": [
        "Isolates isolated points as noise automatically",
        "Clusters all points",
        "Noise merged into clusters",
        "Algorithm fails"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Low-density or isolated points are correctly treated as noise."
    },
    {
      "id": 49,
      "questionText": "Scenario: Choosing distance metric affects DBSCAN. Why?",
      "options": [
        "All clusters merge",
        "No effect",
        "Neighborhood depends on distance; cluster shape affected",
        "Noise ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Different metrics change neighbor counts, affecting core points and cluster formation."
    },
    {
      "id": 50,
      "questionText": "Scenario: DBSCAN on highly skewed 2D data. Challenge?",
      "options": [
        "Clusters detected automatically",
        "Algorithm fails",
        "Fixed eps may not capture sparse areas",
        "Noise reduced"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Single eps cannot adapt to varying densities; sparse regions may be misclassified."
    },
    {
      "id": 51,
      "questionText": "Scenario: DBSCAN applied to customer segmentation with varying buying density. Issue?",
      "options": [
        "Noise eliminated automatically",
        "Clusters merged randomly",
        "All clusters detected perfectly",
        "Some smaller or sparser clusters may be missed"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN’s global eps struggles with clusters of different densities; adaptive methods recommended."
    },
    {
      "id": 52,
      "questionText": "Scenario: You want DBSCAN to detect small anomalies in large dataset. How to adjust?",
      "options": [
        "Increase eps arbitrarily",
        "Decrease minPts and eps appropriately",
        "Ignore small clusters",
        "Use K-Means instead"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Smaller minPts and eps allow DBSCAN to detect small dense regions representing anomalies."
    },
    {
      "id": 53,
      "questionText": "Scenario: Using DBSCAN for clustering Wi-Fi signals in a building. Advantage?",
      "options": [
        "Identifies dense signal regions and ignores noise",
        "All points assigned to clusters",
        "Requires number of clusters",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN can detect regions with strong signal density and label isolated weak signals as noise."
    },
    {
      "id": 54,
      "questionText": "Scenario: DBSCAN on image pixel intensities for segmentation. Outcome?",
      "options": [
        "Requires predefined cluster number",
        "Clusters must be circular",
        "All pixels assigned",
        "Arbitrary-shaped regions segmented; noise isolated"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN can segment regions of any shape and label scattered pixels as noise."
    },
    {
      "id": 55,
      "questionText": "Scenario: Using DBSCAN on 3D point cloud of a city. Advantage?",
      "options": [
        "Detects clusters like buildings, trees, and separates sparse points",
        "Noise merged into clusters",
        "All points assigned",
        "Clusters must be spherical"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN works in multi-dimensional data and identifies meaningful dense clusters."
    },
    {
      "id": 56,
      "questionText": "Scenario: DBSCAN fails with high-dimensional word embeddings. Solution?",
      "options": [
        "Apply dimensionality reduction before clustering",
        "Use K-Means",
        "Increase eps globally",
        "Ignore problem"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-dimensional spaces make distance less meaningful; reduction helps clustering performance."
    },
    {
      "id": 57,
      "questionText": "Scenario: Border points connected to multiple core points. Assignment?",
      "options": [
        "Assigned to one cluster reachable first",
        "Algorithm fails",
        "Assigned to all clusters simultaneously",
        "Become noise"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN assigns border points to a single cluster; typically the first reachable core point."
    },
    {
      "id": 58,
      "questionText": "Scenario: DBSCAN on streaming data. Limitation?",
      "options": [
        "Noise ignored",
        "Automatically updates clusters",
        "All points reassigned automatically",
        "Standard DBSCAN cannot update incrementally; adaptation needed"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Incremental or streaming adaptations of DBSCAN are required for dynamic datasets."
    },
    {
      "id": 59,
      "questionText": "Scenario: Clusters are elongated and dense. DBSCAN vs K-Means?",
      "options": [
        "Both fail",
        "K-Means better",
        "All points assigned to noise",
        "DBSCAN captures shape; K-Means fails with elongated clusters"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN’s density-based approach handles arbitrary shapes like elongated clusters well."
    },
    {
      "id": 60,
      "questionText": "Scenario: Choosing minPts parameter. Rule of thumb?",
      "options": [
        "minPts = dataset size",
        "minPts ≥ dimensionality + 1",
        "minPts = 1 always",
        "minPts ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Choosing minPts slightly larger than data dimensionality ensures meaningful cluster formation."
    },
    {
      "id": 61,
      "questionText": "Scenario: DBSCAN applied to weather station locations. Advantage?",
      "options": [
        "All stations assigned",
        "Noise merged into clusters",
        "Detects dense station clusters and separates isolated stations as noise",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN can find groups of stations in dense regions while labeling isolated ones as noise."
    },
    {
      "id": 62,
      "questionText": "Scenario: DBSCAN applied to vehicle GPS tracks. Best outcome?",
      "options": [
        "All vehicles assigned to same cluster",
        "Requires predefined cluster number",
        "Clusters must be circular",
        "Detect hotspots of vehicle activity and identify sparse routes"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN identifies dense routes or locations and marks sparse movements as noise."
    },
    {
      "id": 63,
      "questionText": "Scenario: DBSCAN applied to detect fraudulent transactions. Advantage?",
      "options": [
        "All transactions clustered",
        "Isolates unusual low-density transactions as potential fraud",
        "Clusters merged arbitrarily",
        "Noise ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Low-density points are flagged naturally, useful for anomaly detection in finance."
    },
    {
      "id": 64,
      "questionText": "Scenario: eps too large. Effect on clusters?",
      "options": [
        "Clusters may merge; noise reduced",
        "Noise increases",
        "More clusters detected",
        "Algorithm fails"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Large eps connects distant points, merging separate clusters and reducing noise."
    },
    {
      "id": 65,
      "questionText": "Scenario: eps too small. Effect on clusters?",
      "options": [
        "Clusters merge",
        "Noise decreases",
        "Many points labeled as noise; clusters fragmented",
        "Algorithm fails"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Small eps prevents points from forming dense clusters; many become noise."
    },
    {
      "id": 66,
      "questionText": "Scenario: High-dimensional clustering. DBSCAN limitation?",
      "options": [
        "Clusters detected perfectly",
        "All points assigned",
        "Distances lose meaning; density estimation difficult",
        "Noise ignored"
      ],
      "correctAnswerIndex": 2,
      "explanation": "High dimensions make points appear equidistant, complicating density-based clustering."
    },
    {
      "id": 67,
      "questionText": "Scenario: Data with multiple density clusters. Solution?",
      "options": [
        "Use HDBSCAN for adaptive density clustering",
        "Increase eps globally",
        "Reduce minPts to 1",
        "Ignore problem"
      ],
      "correctAnswerIndex": 0,
      "explanation": "HDBSCAN adapts to varying densities, unlike standard DBSCAN."
    },
    {
      "id": 68,
      "questionText": "Scenario: Using DBSCAN on customer browsing patterns. Advantage?",
      "options": [
        "Noise ignored",
        "Requires predefined cluster number",
        "All points assigned",
        "Detects dense behavioral patterns and isolates outliers"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Dense browsing behaviors form clusters; rare patterns become noise."
    },
    {
      "id": 69,
      "questionText": "Scenario: Noise points in DBSCAN. Definition?",
      "options": [
        "Cluster centroids",
        "All points in clusters",
        "Points not reachable from any core point",
        "Points with minPts neighbors"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Noise points are isolated points not part of any cluster."
    },
    {
      "id": 70,
      "questionText": "Scenario: Choosing distance metric in DBSCAN. Effect?",
      "options": [
        "Affects neighborhood definition and cluster shape",
        "Noise ignored",
        "No effect",
        "All points merged"
      ],
      "correctAnswerIndex": 0,
      "explanation": "The distance metric changes how neighbors are counted, affecting cluster formation."
    },
    {
      "id": 71,
      "questionText": "Scenario: DBSCAN applied to earthquake epicenters. Advantage?",
      "options": [
        "Clusters must be circular",
        "Detects clusters of seismic activity and isolates isolated events",
        "Noise merged into clusters",
        "All events assigned"
      ],
      "correctAnswerIndex": 1,
      "explanation": "DBSCAN identifies dense seismic regions and separates rare events as noise."
    },
    {
      "id": 72,
      "questionText": "Scenario: Varying eps across dataset. How to achieve?",
      "options": [
        "Ignore variation",
        "Random eps each run",
        "Use adaptive DBSCAN variants like HDBSCAN",
        "Standard DBSCAN suffices"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Adaptive algorithms adjust density thresholds to handle varying densities."
    },
    {
      "id": 73,
      "questionText": "Scenario: Applying DBSCAN on medical imaging. Benefit?",
      "options": [
        "Clusters must be spherical",
        "Requires fixed cluster number",
        "Detects regions of interest and separates background noise",
        "All pixels clustered"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN segments irregular shapes and isolates sparse/noisy regions."
    },
    {
      "id": 74,
      "questionText": "Scenario: Using DBSCAN for anomaly detection in IoT sensors. Approach?",
      "options": [
        "Ignore isolated readings",
        "Label low-density readings as anomalies",
        "Cluster all points",
        "Random assignment"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Isolated readings or sparse patterns naturally become noise, indicating anomalies."
    },
    {
      "id": 75,
      "questionText": "Scenario: DBSCAN on financial transactions. Noise points indicate?",
      "options": [
        "Noise merged",
        "All transactions are legitimate",
        "Potential fraudulent or unusual transactions",
        "Clusters merged"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sparse points in dense transaction space are flagged as unusual or fraudulent."
    },
    {
      "id": 76,
      "questionText": "Scenario: DBSCAN applied to traffic accident locations. Advantage?",
      "options": [
        "Identifies accident hotspots and isolates rare events",
        "All accidents assigned",
        "Noise merged into clusters",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN finds dense accident regions and treats isolated incidents as noise."
    },
    {
      "id": 77,
      "questionText": "Scenario: eps and minPts selection using k-distance plot. What is the elbow point?",
      "options": [
        "Minimum distance",
        "Random point",
        "Point where distance sharply increases, suitable for eps",
        "Maximum distance"
      ],
      "correctAnswerIndex": 2,
      "explanation": "The elbow in the k-distance graph indicates the transition from dense to sparse regions, guiding eps selection."
    },
    {
      "id": 78,
      "questionText": "Scenario: Border points connected to multiple clusters. Assignment in DBSCAN?",
      "options": [
        "Assigned to all clusters",
        "Become noise",
        "Assigned to the first reachable cluster",
        "Clusters merge automatically"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Border points are assigned to one cluster, typically the first one that reaches them."
    },
    {
      "id": 79,
      "questionText": "Scenario: DBSCAN on social media check-ins. Benefit?",
      "options": [
        "Clusters must be predefined",
        "All users assigned",
        "Noise ignored",
        "Detects popular locations and identifies sparse users"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN identifies dense activity areas and treats isolated check-ins as noise."
    },
    {
      "id": 80,
      "questionText": "Scenario: Standard DBSCAN fails on variable density data. Solution?",
      "options": [
        "Ignore the problem",
        "Increase eps globally",
        "Decrease minPts arbitrarily",
        "Use HDBSCAN for hierarchical density-based clustering"
      ],
      "correctAnswerIndex": 3,
      "explanation": "HDBSCAN adapts to varying density, unlike standard DBSCAN."
    },
    {
      "id": 81,
      "questionText": "Scenario: DBSCAN on genomic data. Advantage?",
      "options": [
        "Clusters must be circular",
        "All genes assigned",
        "Identifies dense gene clusters and isolates rare genes",
        "Noise merged"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN can identify dense gene expression patterns and separate sparse or rare genes as noise."
    },
    {
      "id": 82,
      "questionText": "Scenario: Choosing minPts too high. Effect?",
      "options": [
        "Algorithm fails",
        "Clusters merge",
        "Small clusters ignored; many points labeled noise",
        "More clusters detected"
      ],
      "correctAnswerIndex": 2,
      "explanation": "High minPts requires dense regions; sparse or small clusters are lost."
    },
    {
      "id": 83,
      "questionText": "Scenario: Choosing minPts too low. Effect?",
      "options": [
        "Many small clusters formed; noise reduced",
        "Clusters merge",
        "Algorithm fails",
        "All points noise"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Low minPts allows small groups to form clusters, potentially splitting natural clusters."
    },
    {
      "id": 84,
      "questionText": "Scenario: DBSCAN applied on customer location data. Advantage?",
      "options": [
        "Requires predefined cluster count",
        "All points assigned",
        "Clusters must be circular",
        "Identifies dense shopping areas and isolates isolated customers"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN captures dense shopping locations and labels scattered customers as noise."
    },
    {
      "id": 85,
      "questionText": "Scenario: High-dimensional text embeddings. DBSCAN limitation?",
      "options": [
        "Distances lose meaning; clusters may be unreliable",
        "Algorithm faster",
        "Noise ignored",
        "Clusters always detected"
      ],
      "correctAnswerIndex": 0,
      "explanation": "In high dimensions, distances are less discriminative, affecting density and clustering."
    },
    {
      "id": 86,
      "questionText": "Scenario: Using DBSCAN on image feature vectors. Benefit?",
      "options": [
        "Requires predefined cluster count",
        "All features assigned",
        "Groups similar image features and isolates outliers",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 2,
      "explanation": "DBSCAN detects dense feature groups and treats isolated features as noise."
    },
    {
      "id": 87,
      "questionText": "Scenario: eps too small. Effect?",
      "options": [
        "Clusters fragmented; many points labeled noise",
        "Clusters merge",
        "All points assigned",
        "Algorithm fails"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Small eps prevents formation of dense clusters; isolated points become noise."
    },
    {
      "id": 88,
      "questionText": "Scenario: eps too large. Effect?",
      "options": [
        "More clusters detected",
        "Algorithm fails",
        "Clusters merge; fewer noise points",
        "Noise increases"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Large eps connects distant points, merging separate clusters and reducing noise."
    },
    {
      "id": 89,
      "questionText": "Scenario: DBSCAN on irregularly shaped 2D clusters. Advantage?",
      "options": [
        "Clusters must be circular",
        "Captures arbitrary shapes unlike K-Means",
        "All points assigned",
        "Noise ignored"
      ],
      "correctAnswerIndex": 1,
      "explanation": "DBSCAN does not assume cluster shape, so it captures elongated or irregular clusters."
    },
    {
      "id": 90,
      "questionText": "Scenario: Border point connected to multiple core points. Assignment?",
      "options": [
        "Assigned to first reachable cluster",
        "Assigned to all clusters",
        "Clusters merge",
        "Becomes noise"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN assigns a border point to one cluster, typically the first core point that reaches it."
    },
    {
      "id": 91,
      "questionText": "Scenario: DBSCAN on IoT sensor anomaly detection. Advantage?",
      "options": [
        "Sparse readings flagged as anomalies automatically",
        "Noise ignored",
        "All readings clustered",
        "Clusters merged arbitrarily"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN labels low-density points as noise, which is useful for detecting anomalies."
    },
    {
      "id": 92,
      "questionText": "Scenario: DBSCAN with streaming data. Limitation?",
      "options": [
        "Needs adaptation; standard DBSCAN is static",
        "Noise ignored",
        "Automatically updates clusters",
        "All points reassigned automatically"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN is not incremental; streaming or dynamic data requires modified algorithms."
    },
    {
      "id": 93,
      "questionText": "Scenario: Using DBSCAN on earthquake data. Benefit?",
      "options": [
        "Detects dense seismic zones; isolates rare events",
        "All events clustered",
        "Noise merged",
        "Clusters must be circular"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN identifies dense clusters of earthquakes and labels isolated events as noise."
    },
    {
      "id": 94,
      "questionText": "Scenario: Noise in DBSCAN definition?",
      "options": [
        "Cluster centroids",
        "Points not reachable from any core point",
        "Points with minPts neighbors",
        "All points assigned"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Noise points are isolated points not part of any cluster."
    },
    {
      "id": 95,
      "questionText": "Scenario: Varying density clusters. Best DBSCAN variant?",
      "options": [
        "Standard DBSCAN",
        "K-Means",
        "HDBSCAN",
        "Agglomerative clustering"
      ],
      "correctAnswerIndex": 2,
      "explanation": "HDBSCAN adapts to different densities and creates a hierarchy of clusters."
    },
    {
      "id": 96,
      "questionText": "Scenario: Choosing eps using k-distance plot. How?",
      "options": [
        "Select maximum distance",
        "Select value at elbow point where distances sharply rise",
        "Randomly select eps",
        "Select minimum distance"
      ],
      "correctAnswerIndex": 1,
      "explanation": "The elbow point indicates the transition from dense to sparse points, guiding eps choice."
    },
    {
      "id": 97,
      "questionText": "Scenario: DBSCAN on customer behavior patterns. Benefit?",
      "options": [
        "Groups dense behavior patterns; isolates rare customers",
        "Requires fixed number of clusters",
        "Noise ignored",
        "All points assigned"
      ],
      "correctAnswerIndex": 0,
      "explanation": "DBSCAN identifies dense behavioral clusters and labels rare behaviors as noise."
    },
    {
      "id": 98,
      "questionText": "Scenario: DBSCAN vs K-Means for non-spherical clusters. Advantage?",
      "options": [
        "K-Means better",
        "Noise ignored",
        "Both fail",
        "DBSCAN captures arbitrary shapes"
      ],
      "correctAnswerIndex": 3,
      "explanation": "DBSCAN does not assume cluster shape and handles irregular or elongated clusters."
    },
    {
      "id": 99,
      "questionText": "Scenario: High-dimensional DBSCAN problem. Solution?",
      "options": [
        "Dimensionality reduction (PCA, t-SNE) or HDBSCAN",
        "Increase minPts arbitrarily",
        "Ignore scaling",
        "Use raw distances"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-dimensional spaces make distances less meaningful; reduction or adaptive methods improve clustering."
    },
    {
      "id": 100,
      "questionText": "Scenario: Choosing minPts in DBSCAN. Rule of thumb?",
      "options": [
        "minPts = 1 always",
        "minPts = dataset size",
        "Ignore minPts",
        "minPts ≥ dimensionality + 1"
      ],
      "correctAnswerIndex": 3,
      "explanation": "minPts should slightly exceed data dimensionality to ensure meaningful clusters."
    }
  ]
}