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Python Quiz Questions with Answers for Interview Preparation
Which metric is commonly used to assess how closely predicted values match actual outcomes in regression analysis?
a. R-squared
b. Mean Squared Error (MSE)
c. F1-score
d. None of the above
Option b – Mean Squared Error (MSE)
In the context of classification model evaluation, what does cross-entropy measure?
a. The percentage of correctly predicted labels
b. The average logarithmic loss between actual and predicted probabilities
c. The overall accuracy of the classifier
d. None of the above
Option b – The average logarithmic loss between actual and predicted probabilities
Which function in scikit-learn can be used to calculate cross-entropy loss for classification tasks?
a. evaluate_cross_entropy()
b. cross_entropy_score()
c. log_loss()
d. compute_cross_entropy()
Option c – log_loss()
What evaluation strategy helps prevent data leakage during model validation?
a. Holdout validation
b. K-fold cross-validation
c. Leave-One-Out cross-validation
d. None of the above
Option b – K-fold cross-validation
Which metric is best for evaluating models on imbalanced datasets and combines precision and recall into a single score?
a. Accuracy
b. Precision
c. Recall
d. F1-score
Option d – F1-score
Which Python package is primarily used to create two-dimensional plots, charts, and visualizations?
a. Pandas
b. Matplotlib
c. NumPy
d. Seaborn
Option b – Matplotlib
In the Matplotlib library, which module is typically used to generate figures and visual plots?
a. matplotlib.pyplot
b. matplotlib.figure
c. matplotlib.plot
d. matplotlib.graph
Option a – matplotlib.pyplot
Which function in Matplotlib is used to generate a basic line chart?
a. plt.plot()
b. plt.line()
c. plt.draw()
d. plt.create()
Option a – plt.plot()
Which command in Matplotlib allows you to assign a title to a chart?
a. plt.set_title()
b. plt.title()
c. plt.plot_title()
d. plt.set_plot_title()
Option b – plt.title()
What function in Matplotlib is specifically used for plotting a scatter diagram?
a. plt.scatter()
b. plt.plot()
c. plt.draw()
d. plt.create_scatter()
Option a – plt.scatter()
Which Matplotlib commands are used to label the horizontal and vertical axes?
a. plt.xlabel() and plt.ylabel()
b. plt.add_xaxis_label() and plt.add_yaxis_label()
c. plt.x_label() and plt.y_label()
d. plt.axis_labels()
Option a – plt.xlabel() and plt.ylabel()
What Matplotlib command is used to render the final plot on the screen?
a. plt.show()
b. plt.display()
c. plt.plot()
d. plt.view()
Option a – plt.show()
Which Matplotlib function is specifically used to draw a histogram?
a. plt.hist()
b. plt.plot()
c. plt.bar()
d. plt.create_histogram()
Option a – plt.hist()
Which pip command installs NLTK along with its data resources on a Mac or Unix system?
a. pip install nltk
b. pip install nltk-data
c. pip install nltk -additional-data
d. pip install nltk && nltk.download()
Option d – pip install nltk && nltk.download()
What tool enables NLTK installation via Homebrew on Unix-based systems?
a. brew install nltk
b. pip install nltk
c. conda install nltk
d. apt-get install nltk
Option a – brew install nltk
After installing NLTK using pip on Mac/Unix, what is the next required step?
a. Use import nltk in a Python script
b. Define NLTK environment variables
c. Run nltk.download() to get data files
d. Reopen the terminal window
Option c – Run nltk.download() to get data files
In a Unix system, what does using the sudo command before installing a Python package indicate?
a. You’re using a Unix shell
b. The package installs only for the current user
c. Elevated (admin/root) privileges are required
d. You’re uninstalling an existing package
e. You’re working inside a virtual environment
Option b – The package installs only for the current user
Which command lets you verify the current version of NLTK on Mac/Unix?
a. pip version nltk
b. nltk –version
c. pip show nltk
d. nltk version
Option c – pip show nltk
What is the purpose of running nltk.download() after the library is installed?
a. Installs required third-party packages
b. Fetches NLTK’s extra datasets and corpora
c. Updates NLTK to the newest version
d. Modifies NLTK configuration settings
Option b – Fetches NLTK’s extra datasets and corpora
Where is NLTK data commonly stored after downloading on Unix-based systems?
a. /usr/share/nltk_data
b. ~/nltk_data
c. /usr/local/nltk_data
d. /var/lib/nltk_data
Option b – ~/nltk_data
Which pip command installs NLTK in a Windows environment?
a. pip install nltk
b. pip3 install nltk
c. conda install nltk
d. download nltk
Option a – pip install nltk
What is the typical package manager used to install NLTK on Windows systems?
a. pip
b. conda
c. npm
d. chocolatey
Option a – pip
What pip command installs both the NLTK package and its datasets in Windows?
a. pip install nltk-data
b. pip install nltk –all-data
c. pip install nltk && nltk.download()
d. pip install nltk &&& download-nltk-data
Option c – pip install nltk && nltk.download()
How should you initiate the NLTK data downloader in a Windows environment?
a. Call nltk.download() from within a Python script
b. Execute nltk.download() in the command line
c. Use the NLTK Downloader shortcut in the Start menu
d. Access the NLTK website and download manually
Option c – Use the NLTK Downloader shortcut in the Start menu
Which tool helps automate NLTK installation and manage Python packages on Windows?
a. PowerShell
b. Command Prompt
c. Anaconda Prompt
d. Chocolatey
Option d – Chocolatey
Which technique is often applied to address missing values prior to training a machine learning model?
a. Replacing with the mean
b. Replacing with the mode
c. Replacing with the median
d. Replacing with random values
Option a – Replacing with the mean
What is the primary purpose of applying feature scaling in machine learning?
a. To reduce the total number of input features
b. To bring all feature values within a comparable range
c. To introduce additional features to the dataset
d. To eliminate duplicate or irrelevant features
Option b – To bring all feature values within a comparable range
a. Controls how fast the network learns
b. Determines the hidden layer’s output
c. Selects the error function for training
d. Decides how many neurons are used
Option b – Determines the hidden layer’s output
What challenge does the K-nearest neighbors algorithm typically encounter with high-dimensional input data?
a. Tends to overfit the model
b. Suffers from the curse of dimensionality
c. Leads to underfitting issues
d. Deals with excess irrelevant features
Option b – Suffers from the curse of dimensionality
What type of result is typically produced by unsupervised learning models?
a. Category labels
b. Forecasted values
c. Groupings or clusters
d. Confidence levels
Option c – Groupings or clusters
Which method is often applied to measure how well a clustering algorithm performs?
a. Accuracy score
b. F1 measure
c. Silhouette score
d. Mean squared difference
Option c – Silhouette score
What does the term “curse of dimensionality” refer to in the context of unsupervised learning?
a. Trouble managing missing or incomplete entries
b. Higher computational demands as the number of features increases
c. A greater need for data preprocessing techniques
d. The inability of some algorithms to process many variables
Option b – Higher computational demands as the number of features increases
Which of these is a commonly used distance measure in clustering techniques?
a. Euclidean distance
b. Bayesian distance
c. Manhattan distance
d. Cosine similarity
Option a – Euclidean distance
Which unsupervised method is frequently used to estimate missing data points?
a. PCA (Principal Component Analysis)
b. K-means method
c. Decision trees
d. EM (Expectation-Maximization) approach
Option d – EM (Expectation-Maximization) approach
Which of the following algorithms is typically utilized for estimating data density in unsupervised models?
a. Decision tree
b. Naive Bayes classifier
c. Gaussian Mixture Model (GMM)
d. K-nearest neighbor
Option c – Gaussian Mixture Model (GMM)
What is a key benefit of hierarchical clustering over the K-means algorithm?
a. It processes data faster and scales better
b. It does not need a predefined number of clusters
c. It works well with high-dimensional datasets
d. It always finds the best possible clustering solution
Option b – It does not need a predefined number of clusters
Which model is widely used for identifying anomalies in time-based datasets?
a. K-means clustering
b. DBSCAN algorithm
c. Autoencoders
d. Support Vector Machine (SVM)
Option c – Autoencoders
Which algorithm is often applied for discovering patterns in sequences?
a. K-means
b. Decision tree classifier
c. Hidden Markov Models (HMMs)
d. Random forest
Option c – Hidden Markov Models (HMMs)
Which unsupervised method is often chosen for extracting features from data?
a. K-means
b. Linear regression
c. Principal Component Analysis (PCA)
d. Naive Bayes
Option c – Principal Component Analysis (PCA)
In natural language processing, which technique is generally used for topic discovery?
a. SVM (Support Vector Machine)
b. Decision tree
c. K-means
d. Latent Dirichlet Allocation (LDA)
Option d – Latent Dirichlet Allocation (LDA)
What is the primary objective of detecting outliers in unsupervised learning?
a. To find the most frequent observations
b. To locate key features in the dataset
c. To spot data points that significantly deviate from the norm
d. To identify clusters or similar data segments
Option c – To spot data points that significantly deviate from the norm
Which technique is widely adopted for extracting features in computer vision applications?
a. Decision tree
b. Support Vector Machine
c. K-means clustering
d. Convolutional Neural Network (CNN)
Option a – Decision tree
What is the key goal of performing data preprocessing in unsupervised learning?
a. To boost the speed and efficiency of algorithms
b. To eliminate anomalies from the dataset
c. To bring all feature values to a common scale
d. To enhance prediction accuracy
Option c – To bring all feature values to a common scale
Which method is frequently applied in unsupervised learning to explore and understand data patterns?
a. Decision tree
b. K-means clustering
c. Principal Component Analysis (PCA)
d. Random forest
Option c – Principal Component Analysis (PCA)
What is the core purpose of generative models in the context of unsupervised learning?
a. To group data points into predefined categories
b. To create synthetic data that mimics real samples
c. To detect groupings or clusters in the data
d. To estimate continuous outcomes from input variables
Option b – To create synthetic data that mimics real samples
Which technique is typically utilized to fill in missing entries in recommendation system matrices?
a. Singular Value Decomposition (SVD)
b. Naive Bayes
c. K-means clustering
d. Random forest
Option a – Singular Value Decomposition (SVD)
What is a key benefit of semi-supervised learning compared to a purely unsupervised approach?
a. Better at handling incomplete datasets
b. Requires a smaller set of labeled examples for training
c. Capable of processing high-dimensional and large datasets
d. Ensures finding the optimal global solution
Option b – Requires a smaller set of labeled examples for training
Which approach is commonly employed to discover association rules in transaction data?
a. Principal Component Analysis (PCA)
b. Apriori algorithm
c. Random forest
d. Support Vector Machine
Option b – Apriori algorithm
What is the primary aim of manifold learning in unsupervised techniques?
a. To detect clusters in data
b. To reduce high-dimensional data into a more interpretable form
c. To assign labels to different data categories
d. To generate predicted values from inputs
Option b – To reduce high-dimensional data into a more interpretable form
Which method is often used for clustering data in graph-based unsupervised models?
a. Decision tree
b. K-means clustering
c. Spectral clustering
d. Support Vector Machine
Option c – Spectral clustering
What is a common limitation when using unsupervised methods for detecting anomalies?
a. Difficulty with high-dimensional data processing
b. Dependence on a large volume of labeled data
c. Potential for incorrect classifications of normal or anomalous points
d. Increased computational demands compared to supervised models
Option c – Potential for incorrect classifications of normal or anomalous points
Which method is typically applied to estimate missing values in time series using unsupervised learning?
a. Decision tree
b. K-means clustering
c. Seasonal Holt-Winters method
d. Support Vector Machine
Option c – Seasonal Holt-Winters method
What is a significant limitation of using unsupervised learning models for classification purposes?
a. They need a large quantity of labeled data for training
b. They tend to overfit when applied to datasets with many dimensions
c. They struggle to interpret non-linear patterns within data
d. They generally require more computational resources than supervised methods
Option a – They need a large quantity of labeled data for training
Which method is widely adopted for identifying anomalies in data using density-based clustering?
a. K-means
b. DBSCAN
c. Autoencoder
d. Support vector machine
Option b – DBSCAN
What is the primary purpose of using t-SNE in unsupervised learning?
a. Grouping data into labeled classes
b. Reducing dimensionality for visual representation of complex datasets
c. Detecting natural groupings or patterns in data
d. Estimating continuous variables from input features
Option b – Reducing dimensionality for visual representation of complex datasets
Which technique in unsupervised learning helps track changes in data patterns over time?
a. Decision tree
b. K-means clustering
c. Random forest
d. Online clustering algorithms
Option d – Online clustering algorithms
What is the key goal of using k-medoids clustering?
a. To categorize data into distinct labeled classes
b. To suggest customized content for individual users
c. To find natural groupings among data points
d. To generate predictions for numerical outcomes
Option c – To find natural groupings among data points
Which of the following clustering methods does not rely on distance calculations?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
d. Mean Shift
Option c – DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
What is the main aim of applying K-means clustering?
a. Reducing the sum of squared distances within clusters
b. Maximizing the separation between different clusters
c. Achieving the best silhouette score possible
d. Decreasing the total number of identified clusters
Option a – Reducing the sum of squared distances within clusters
Which approach for initializing K-means starts by selecting data points at random as centroids?
a. K-means++
b. K-means#
c. Random initialization
d. Sklearn initialization
Option c – Random initialization
In K-means clustering, what does the elbow method help determine?
a. The best number of clusters for the dataset
b. How tightly grouped the clusters are
c. The distinctness between clusters
d. The approximate size of each cluster
Option a – The best number of clusters for the dataset
Which clustering method is suitable for data that is not linearly separable?
a. K-means clustering
b. Agglomerative hierarchical clustering
c. Mean Shift
d. Spectral clustering
Option d – Spectral clustering
What is typically generated as the result of hierarchical clustering?
a. A dendrogram visualizing the cluster hierarchy
b. A list of central cluster points
c. Labels assigned to each data point
d. A group of prototype data samples
Option a – A dendrogram visualizing the cluster hierarchy
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