MCQs

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Overall MCQs for Reference

What type of learning is K-Means clustering categorized as?
a. Supervised
b. Unsupervised
c. Reinforcement
d. Semi-supervised
Answer: b. Unsupervisedibm+1
Which metric is most commonly used for measuring the distance in K-Means clustering?
a. Manhattan Distance
b. Jaccard Distance
c. Euclidean Distance
d. Cosine Similarity
Answer: c. Euclidean Distanceyoutubestanford
What does ‘K’ represent in the K-Means algorithm?
a. Number of centroids
b. Number of clusters
c. Number of features
d. Number of data points
Answer: b. Number of clusterswikipedia+1
What is the objective function of K-Means clustering?
a. Maximizing variance between clusters
b. Minimizing sum of squared errors within clusters
c. Maximizing log-likelihood
d. Minimizing total data points
Answer: b. Minimizing sum of squared errors within clustersneptune+1
What happens during the expectation step in K-Means clustering?
a. Centroids are recalculated
b. Distance metric is chosen
c. Data points are assigned to clusters
d. Number of clusters is reduced
Answer: c. Data points are assigned to clustersneptune
The K-Means algorithm uses which type of centroids?
a. Mode
b. Median
c. Mean
d. Minimum
Answer: c. Meanaimcqs+1
Which is NOT a requirement for K-Means clustering to work?
a. Number of clusters defined
b. Initial positions of centroids
c. Data labels
d. Defined distance metric
Answer: c. Data labelssanfoundry+1
What is the most critical disadvantage of K-Means clustering?
a. Requires labeled data
b. Sensitive to outliers
c. Cannot handle categorical data
d. It’s a supervised algorithm
Answer: b. Sensitive to outlierslyzr+1
What is the initial step in K-Means clustering?
a. Assign data labels
b. Initialize centroids
c. Assign all points to a single cluster
d. Normalize data
Answer: b. Initialize centroidsupgrad
Which application area is K-Means clustering commonly used for?
a. Regression analysis
b. Customer segmentation
c. Time series forecasting
d. Image recognition
Answer: b. Customer segmentationaws.amazon+1
Which algorithm is NOT a centroid-based clustering approach?
a. K-Means
b. Mean-shift
c. DBSCAN
d. Gaussian Mixture
Answer: c. DBSCANitfeature+1
What is one way to reduce the dimensionality before applying K-Means?
a. Linear regression
b. PCA (Principal Component Analysis)
c. KNN
d. Random Forest
Answer: b. PCA (Principal Component Analysis)upgrad
What may happen if ‘K’ is set too high?
a. Underfitting
b. Overfitting
c. No clusters formed
d. Centroids duplicate
Answer: b. Overfittinglyzr
Which method is commonly used to choose the optimal number of clusters ‘K’?
a. Grid Search
b. Silhouette Score
c. Elbow Method
d. Gradient Descent
Answer: c. Elbow Methodaionlinecourse+1
In K-Means clustering, what is recalculated after each iteration?
a. Number of features
b. Cluster centroids
c. Data labels
d. Covariance matrix
Answer: b. Cluster centroidswikipedia+1
When does the K-Means algorithm stop?
a. When sum of distances increases
b. When cluster assignments no longer change
c. After a set number of iterations always
d. When new centroids are maximum
Answer: b. When cluster assignments no longer changeneptune+1
Which of the following best describes K-Means initialization?
a. Deterministic
b. Random
c. Gradient-based
d. Labeled
Answer: b. Randomneptune
Which domain commonly benefits from anomaly detection using K-Means?
a. Cybersecurity
b. Weather forecasting
c. Data compression
d. Speech synthesis
Answer: a. Cybersecuritylinkedin+1
Which is NOT a limitation of K-Means clustering?
a. Sensitive to the initial centroid positions
b. Cannot handle big data
c. Assumes clusters are spherical
d. Struggles with varying cluster sizes
Answer: b. Cannot handle big datasimplilearn+1
If feature scaling is omitted in K-Means, what could result?
a. No clusters formed
b. Irrelevant feature dominance
c. Faster convergence
d. Higher precision
Answer: b. Irrelevant feature dominanceaionlinecourse+1
What is K-Means++ used for?
a. Converting categorical data
b. Improving centroid initialization
c. Visualizing clusters
d. Optimizing convergence speed
Answer: b. Improving centroid initializationitfeature
K-Means algorithm is least effective on which type of data?
a. Linearly separable
b. Spherical clusters
c. Categorical data
d. Well-scaled numeric data
Answer: c. Categorical datalyzr
What does the “sum of squared distance” in K-Means refer to?
a. Distance from centroid to origin
b. Distance between all centroids
c. Distance from each data point to its cluster’s centroid
d. Distance among clusters
Answer: c. Distance from each data point to its cluster’s centroidaimcqs+1
In which field is K-Means clustering used for grouping genes with similar expression patterns?
a. E-commerce
b. Bioinformatics
c. Education
d. Cloud computing
Answer: b. Bioinformaticsdzone+1
What is the centroid in K-Means?
a. Data point farthest from all others
b. The mean position of data points in a cluster
c. Weighted average of labels
d. Median value of cluster
Answer: b. The mean position of data points in a clusterstanford+1
When is convergence reached in K-Means?
a. No movement of centroids
b. All data points belong to one cluster
c. Distance metric changes
d. Sum of squared distances is maximum
Answer: a. No movement of centroidswikipedia
What data preparation technique is crucial before running K-Means?
a. Feature scaling/normalization
b. Label encoding
c. Polynomial transformation
d. Principal component regression
Answer: a. Feature scaling/normalizationupgrad
Which approach can help K-Means escape poor local minima?
a. Use more neurons
b. Random seed selection
c. Multiple random initializations
d. Lower learning rate
Answer: c. Multiple random initializationsitfeature+1
Which library offers K-Means implementation in Python?
a. Pandas
b. Scikit-learn
c. NumPy
d. Matplotlib
Answer: b. Scikit-learnsimplilearn+1
Which use case is suitable for K-Means clustering in finance?
a. Predicting currency rates
b. Detecting fraud by abnormal purchase patterns
c. Calculating compound interest
d. Market basket analysis
Answer: b. Detecting fraud by abnormal purchase patternsaws.amazon
What is the outcome of the final iteration of K-Means clustering?
a. Each point assigned to a non-unique cluster
b. Each point uniquely assigned to a cluster
c. Centroids are undefined
d. Data is ungrouped
Answer: b. Each point uniquely assigned to a clusterwikipedia+1
What does clustering help reveal in unlabeled datasets?
a. Hidden patterns
b. Causality
c. Supervised structure
d. Data normalization
Answer: a. Hidden patternslyzr+1
How does K-Means treat outliers?
a. Includes them in closest cluster
b. Creates new clusters
c. Ignores them
d. Treats as noise
Answer: a. Includes them in closest clusterneptune
Which is an advancement over traditional K-Means for centroid selection?
a. K-Means++
b. DBSCAN
c. Hierarchical clustering
d. PCA
Answer: a. K-Means++itfeature
In text mining, K-Means can be used for:
a. Tokenization
b. Stop word removal
c. Document clustering
d. Lemmatization
Answer: c. Document clusteringgeeksforgeeks+1
What is a cluster in K-Means?
a. Set of unrelated points
b. Group of points sharing similar features
c. Data points with same labels
d. Features with identical values
Answer: b. Group of points sharing similar featuresstanford+1
Which industry applies K-Means for personalized recommendations?
a. Oil & Gas
b. Music streaming
c. Construction
d. Agriculture
Answer: b. Music streaminglinkedin
K-Means clustering requires ___________________ to be defined in advance.
a. Cluster shapes
b. Number of clusters
c. Centroid locations
d. Dimensionality
Answer: b. Number of clustersgtu-mcq+1
Which step is repeated until K-Means converges?
a. Scaling features
b. Assigning points and recalculating centroids
c. Choosing ‘K’
d. Removing outliers
Answer: b. Assigning points and recalculating centroidswikipedia+1
Which clustering algorithm merges clusters iteratively?
a. K-Means
b. Agglomerative
c. DBSCAN
d. K-Shape
Answer: b. Agglomerativeaimcqs
What is a key difference between K-Means and hierarchical clustering?
a. K-Means uses centroids; hierarchical merges clusters
b. Hierarchical clustering needs number of clusters pre-defined
c. K-Means is not iterative
d. Hierarchical assigns points randomly
Answer: a. K-Means uses centroids; hierarchical merges clustersscribd
Which is a post-processing step after running K-Means?
a. Visualizing clusters
b. Removing all centroids
c. Increasing ‘K’ always
d. Scaling features
Answer: a. Visualizing clustersaionlinecourse+1
Which application benefits from “real-time” clustering adaptation?
a. Deep learning automation
b. Stream clustering for web clickstreams
c. Manual data labeling
d. Static report creation
Answer: b. Stream clustering for web clickstreamslinkedin
What is the role of the ‘inertia’ score in K-Means evaluation?
a. Measures initialization time
b. Measures sum of squared distances from points to centroids
c. Measures variance of clusters
d. Measures total data points
Answer: b. Measures sum of squared distances from points to centroidsaionlinecourse+1
Which algorithm can directly detect outliers as noise points?
a. K-Means
b. DBSCAN
c. PCA
d. K-Medoids
Answer: b. DBSCANaimcqs
In what scenario would choosing a low value for ‘K’ be appropriate?
a. Many distinct classes present
b. Data naturally splits into few groups
c. Searching for anomalies
d. All data is duplicated
Answer: b. Data naturally splits into few groupslyzr
What is a practical step to validate clustering results?
a. Manual labeling
b. Comparing with ground truth if available
c. Increasing features
d. Regression test
Answer: b. Comparing with ground truth if availableupgrad+1
Which aspect is NOT improved by integrating K-Means with deep learning?
a. Automated centroid selection
b. Real-time stream analysis
c. Improved accuracy for outlier detection
d. Supervised labeling
Answer: d. Supervised labelinglinkedin
How can clusters be visualized after K-Means clustering?
a. Scatter plots after dimensionality reduction
b. Confusion matrices
c. Bar graphs
d. Feature importance charts
Answer: a. Scatter plots after dimensionality reductionupgrad
What should you do if clusters formed by K-Means are not meaningful?
a. Increase ‘K’ blindly
b. Re-examine feature selection and preprocessing
c. Ignore clustering outcome
d. Run hierarchical clustering directly
Answer: b. Re-examine feature selection and preprocessinglyzr+1

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