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Python Clustering Interview Questions MCQ Advanced for Students
Which method is typically applied for grouping data in unsupervised learning?
a. K-means
b. Decision tree
c. Naive Bayes
d. Support vector machine
Option a – K-means
What does dimensionality reduction mean in the context of unsupervised learning?
a. A method used to expand the feature set of a dataset
b. A process to minimize random variations in data
c. A method to shrink the number of input features
d. A technique to reduce the processing load
Option c – A method to shrink the number of input features
Which technique is often used to reduce dimensions in unsupervised learning?
a. Decision tree
b. Random forest
c. Principal Component Analysis (PCA)
d. Linear regression
Option c – Principal Component Analysis (PCA)
How is anomaly detection defined in unsupervised learning?
a. A method for spotting unusual or outlier data points
b. A way to assess how well classification models perform
c. A tool for measuring regression model accuracy
d. A method for dealing with incomplete datasets
Option a – A method for spotting unusual or outlier data points
Which tool is commonly used to detect anomalies in unsupervised learning?
a. K-means
b. Support vector machine
c. Decision tree
d. Isolation Forest
Option d – Isolation Forest
What is the main objective of using unsupervised learning?
a. To reduce prediction errors
b. To enhance prediction precision
c. To uncover underlying patterns in the data
d. To fine-tune a cost function
Option c – To uncover underlying patterns in the data
Which of the following is not a benefit of unsupervised learning?
a. It can find hidden data structures
b. It is suitable for data without labels
c. It always boosts prediction accuracy
d. It aids in exploratory data examination
Option c – It always boosts prediction accuracy
What is a major difficulty when applying unsupervised learning?
a. Absence of labeled examples for model training
b. Not enough computing power
c. Problems managing missing or incomplete information
d. Complicated feature extraction steps
Option a – Absence of labeled examples for model training
What is a fundamental distinction between supervised and unsupervised learning?
a. Supervised learning uses labeled data; unsupervised learning doesn’t
b. Supervised learning deals mainly with regression; unsupervised focuses on classification
c. Supervised models handle missing data better than unsupervised models
d. Feature engineering is only needed in supervised learning
Option a – Supervised learning uses labeled data; unsupervised learning doesn’t
Which of the following is a valid unsupervised learning approach?
a. Naive Bayes
b. Linear regression
c. K-means clustering
d. Support vector machine
Option c – K-means clustering
Which technique is widely applied to detect outliers in unsupervised learning models?
a. K-means clustering
b. Isolation Forest
c. Linear regression
d. Naive Bayes
Option b – Isolation Forest
What is a key limitation of unsupervised learning?
a. It depends on labeled data for effective training
b. It may require significant computational resources for big data
c. It only handles linear relationships in datasets
d. It performs worse than supervised methods in accuracy
Option b – It may require significant computational resources for big data
Which approach is typically used in recommending items without labeled data?
a. Apriori method
b. Collaborative filtering
c. PCA (Principal Component Analysis)
d. Random forest
Option b – Collaborative filtering
Which method is often employed to identify communities in network data?
a. Decision trees
b. K-means clustering
c. Louvain method
d. SVM (Support Vector Machine)
Option c – Louvain method
Why is feature selection important in unsupervised learning?
a. It raises the number of input variables
b. It helps minimize the total features used
c. It adds more noise for better generalization
d. It enhances how understandable the data is
Option b – It helps minimize the total features used
What method is commonly applied to detect anomalies in network traffic data?
a. DBSCAN
b. K-means
c. Isolation Forest
d. Linear regression
Option c – Isolation Forest
Which method is frequently utilized for decomposing matrices in recommendation systems?
a. Naive Bayes
b. PCA
c. SVD (Singular Value Decomposition)
d. Decision tree
Option c – SVD (Singular Value Decomposition)
What is the primary issue when dealing with incomplete data in unsupervised settings?
a. Finding a suitable way to fill missing values
b. Managing outliers effectively
c. Selecting the proper clustering method
d. Deciding on the best data transformation
Option a – Finding a suitable way to fill missing values
Which method is useful for recognizing patterns in image data using unsupervised learning?
a. Linear regression
b. K-means
c. Convolutional Neural Networks (CNNs)
d. Apriori algorithm
Option c – Convolutional Neural Networks (CNNs)
What is the key role of self-organizing maps in unsupervised machine learning?
a. Assigning examples to predefined groups
b. Discovering links among data instances
c. Reducing data dimensions for visualization
d. Forecasting continuous values from inputs
Option c – Reducing data dimensions for visualization
Which method is a popular choice for clustering based on density?
a. K-means
b. Decision tree
c. DBSCAN
d. Random forest
Option c – DBSCAN
What is a significant strength of non-parametric models in unsupervised learning?
a. They rely on strict data distribution rules
b. They can model intricate, nonlinear patterns
c. They need extensive labeled training data
d. They usually require less computation than parametric models
Option b – They can model intricate, nonlinear patterns
What is a key benefit of using ensemble techniques in unsupervised learning?
a. They are capable of capturing complex, non-linear patterns in data
b. They need a large set of labeled data to perform effectively
c. They tend to reduce the risk of overfitting compared to individual models
d. They typically require fewer computational resources than single models
Option c – They tend to reduce the risk of overfitting compared to individual models
Which method is commonly applied for grouping similar documents in NLP without supervision?
a. Decision tree
b. K-means clustering
c. Latent Dirichlet Allocation (LDA)
d. Support vector machine
Option c – Latent Dirichlet Allocation (LDA)
What is the primary goal of Generative Adversarial Networks (GANs) in unsupervised settings?
a. To assign data points to distinct classes
b. To create new data samples that resemble the training set
c. To group data points based on shared features
d. To estimate continuous values from input data
Option b – To create new data samples that resemble the training set
Which method is frequently used for factorizing a non-negative matrix in unsupervised tasks?
a. Naive Bayes
b. Principal Component Analysis (PCA)
c. K-means clustering
d. Non-negative Matrix Factorization (NMF)
Option d – Non-negative Matrix Factorization (NMF)
What is the core objective of applying unsupervised methods in recommendation engines?
a. To make the dataset easier to interpret
b. To sort data points into fixed categories
c. To provide tailored suggestions for users
d. To perform regression on continuous outputs
Option c – To provide tailored suggestions for users
Which technique is often utilized for segmenting images without labeled data?
a. Linear regression
b. K-means clustering
c. Convolutional Neural Networks (CNN)
d. Random forest
Option b – K-means clustering
Why are word embeddings used in natural language processing?
a. To place documents into predefined groups
b. To automatically create text using given keywords
c. To find clusters or similarities among words
d. To convert words into dense numerical vectors in a large-dimensional space
Option d – To convert words into dense numerical vectors in a large-dimensional space
What algorithm is frequently chosen for estimating data distribution in unsupervised learning?
a. Decision tree
b. Naive Bayes
c. Gaussian Mixture Models (GMM)
d. K-nearest neighbors
Option c – Gaussian Mixture Models (GMM)
What is the aim of identifying communities in network-based unsupervised learning?
a. To sort instances into labeled groups
b. To customize suggestions for individuals
c. To locate densely connected groups in a network
d. To estimate numeric outcomes from inputs
Option c – To locate densely connected groups in a network
Which method is used to distinguish between meanings of words in unsupervised NLP?
a. Decision tree
b. K-means clustering
c. Latent Semantic Analysis (LSA)
d. Support vector machine
Option c – Latent Semantic Analysis (LSA)
In DBSCAN, which setting defines the furthest distance two points can be apart to be considered neighbors?
a. Epsilon
b. Min_samples
c. Metric
d. Leaf_size
Option a – Epsilon
Which distance measure is most frequently applied in K-means clustering?
a. Euclidean distance
b. Manhattan distance
c. Hamming distance
d. Minkowski distance
Option a – Euclidean distance
Which clustering technique assigns each point to the closest cluster center repeatedly?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option a – K-means clustering
Which statement accurately reflects a property of the K-means clustering algorithm?
a. It always finds the global optimum solution
b. It is influenced by how initial centroids are chosen
c. It is resistant to the presence of outliers
d. It performs best with very high-dimensional data
Option b – It is influenced by how initial centroids are chosen
In hierarchical clustering, how is the distance between clusters typically measured?
a. Single-linkage
b. Complete-linkage
c. Average-linkage
d. Ward-linkage
Option c – Average-linkage
What does the silhouette score evaluate in a clustering solution?
a. How compact each cluster is
b. How well-separated different clusters are
c. The overall effectiveness of the clustering
d. The algorithm’s computational demands
Option c – The overall effectiveness of the clustering
Which clustering method can be affected by the sequence in which data points appear?
a. K-means clustering
b. Agglomerative hierarchical clustering
c. DBSCAN
d. Mean Shift
Option c – DBSCAN
Why is spectral clustering sometimes preferred over K-means for complex, high-dimensional data?
a. It requires less computational power
b. It can identify clusters in non-linear structures
c. It doesn’t need the number of clusters to be set initially
d. It detects patterns without relying solely on distance measures
Option d – It detects patterns without relying solely on distance measures
What restricts K-means clustering when used with categorical variables?
a. It fails with incomplete data
b. It assumes data points can be compared numerically
c. It cannot discover clusters that aren’t linearly separable
d. It needs the number of clusters to be known in advance
Option b – It assumes data points can be compared numerically
Which Python package includes a ready-to-use K-means clustering implementation?
a. PyTorch
b. Keras
c. Scikit-learn
d. TensorFlow
Option c – Scikit-learn
Why should you scale features before applying K-means clustering?
a. To make cluster visualizations more effective
b. To normalize features and avoid bias toward one feature
c. To speed up convergence of the algorithm
d. To minimize the algorithm’s memory usage
Option b – To normalize features and avoid bias toward one feature
Which clustering method is effective for identifying outliers or unusual data points?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option c – DBSCAN
Which clustering technique uses concepts from graph theory and spectral analysis?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Spectral clustering
Option d – Spectral clustering
Which clustering approach works best for datasets with categorical attributes?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. K-prototypes clustering
Option d – K-prototypes clustering
What is a common disadvantage of the K-means clustering algorithm?
a. It struggles with missing data
b. It is affected by the initial selection of cluster centers
c. It cannot model clusters that are not linearly separable
d. It requires a predefined number of clusters
Option b – It is affected by the initial selection of cluster centers
Which Python package includes an implementation of the Mean Shift clustering algorithm?
a. PyTorch
b. Keras
c. Scikit-learn
d. TensorFlow
Option c – Scikit-learn
Which clustering algorithm is suitable for datasets with many features or high dimensions?
a. K-means clustering
b. Agglomerative hierarchical clustering
c. DBSCAN
d. Affinity propagation
Option d – Affinity propagation
In DBSCAN, what role does the “min_samples” parameter play?
a. Defines the maximum distance for points to be neighbors
b. Sets the minimum number of points required to form a core point
c. Determines the minimum number of clusters to create
d. Specifies the maximum iterations for convergence
Option b – Sets the minimum number of points required to form a core point
Which clustering algorithm uses medoids instead of centroids to represent clusters?
a. K-means clustering
b. K-medians clustering
c. DBSCAN
d. Agglomerative hierarchical clustering
Option b – K-medians clustering
How should the Silhouette coefficient be interpreted in cluster analysis?
a. Values near 1 indicate well-defined clusters
b. Values near -1 suggest poorly separated clusters
c. Values near 0 imply overlapping clusters
d. The coefficient does not provide interpretative value
Option a – Values near 1 indicate well-defined clusters
What is a limitation of K-means when applied to categorical data?
a. It cannot process missing entries
b. It assumes numeric distances between data points
c. It fails to capture non-linear cluster shapes
d. It requires the number of clusters to be fixed in advance
Option b – It assumes numeric distances between data points
Which clustering method is more efficient when dealing with large datasets?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option c – DBSCAN
How does hierarchical clustering typically manage missing data in a dataset?
a. Creates a unique cluster specifically for missing entries
b. Omits records that contain missing values
c. Replaces missing data with averages or medians
d. Treats missing data as a separate category
Option a – Creates a unique cluster specifically for missing entries
Which of the following is not considered a limitation of hierarchical clustering?
a. High computational cost
b. Vulnerability to outliers
c. Requirement to specify the number of clusters beforehand
d. Challenges in interpretation with very large datasets
Option c – Requirement to specify the number of clusters beforehand
Can hierarchical clustering efficiently process very large datasets?
a. Yes
b. No
Option b – No
What is the common way to visualize the outcome of hierarchical clustering?
a. Scatter plot
b. Line graph
c. Dendrogram
d. Heatmap
Option c – Dendrogram
Which function in SciPy is typically used to carry out hierarchical clustering?
a. linkage()
b. cluster()
c. fit()
d. transform()
Option a – linkage()
What is the computational complexity associated with hierarchical clustering?
a. O(n)
b. O(n²)
c. O(n log n)
d. O(n³)
Option b – O(n²)
Does the sequence in which clusters are merged affect the final clustering result in hierarchical clustering?
a. Yes
b. No
Option b – No
Which function in SciPy is used to cut a dendrogram to get a desired number of clusters?
a. cut_tree()
b. cluster()
c. fit()
d. transform()
Option a – cut_tree()
What does hierarchical clustering usually output?
a. Cluster centroids
b. Cluster labels
c. Silhouette scores
d. Feature importance values
Option b – Cluster labels
Which linkage method in hierarchical clustering tends to produce elongated, chain-like clusters?
a. Single linkage
b. Complete linkage
c. Average linkage
d. Ward’s method
Option a – Single linkage
Is hierarchical clustering guaranteed to find the absolute best clustering solution?
a. Yes
b. No
Option b – No
In Ward’s method, what measure is used to calculate the distance between clusters?
a. Mean differences
b. Variance differences
c. Sum of squared differences
d. Median differences
Option c – Sum of squared differences
What technique can help decide the optimal number of clusters in hierarchical clustering?
a. Elbow method
b. Silhouette analysis
c. Dendrogram inspection
d. Gap statistic
Option c – Dendrogram inspection
Is agglomerative clustering considered a bottom-up clustering approach?
a. Yes
b. No
Option a – Yes
What is the maximum possible number of clusters that hierarchical clustering can produce?
a. Equal to the number of data points (n)
b. One less than the number of data points (n – 1)
c. Half the number of data points (n / 2)
d. Zero
Option b – One less than the number of data points (n – 1)
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