Placement Preparationan HCL GUVI product
Q121
Q121 A pie chart in Matplotlib displays incorrect proportions. What could be the issue?
Wrong data labels
Missing data
Incorrect sum of values
Invalid chart type
Q122
Q122 A scatter plot shows overlapping points, making it hard to interpret. What can improve its readability?
Increase marker size
Add jitter
Use smaller axes
Change chart type
Q123
Q123 A line chart is difficult to interpret due to too many data points. What is the best approach to simplify it?
Aggregate data
Remove the chart
Use larger axes
Switch to bar chart
Q124
Q124 What is the primary objective of machine learning?
To clean data
To make predictions based on data
To create databases
To improve hardware
Q125
Q125 Which of the following is a supervised learning algorithm?
K-Means
Decision Trees
DBSCAN
Principal Component Analysis
Q126
Q126 What is overfitting in machine learning?
Model performs poorly on training data
Model performs well on training data but poorly on new data
Model is too simple
Model has no bias
Q127
Q127 What is the purpose of a loss function in machine learning?
To evaluate model predictions
To split datasets
To improve visualization
To standardize data
Q128
Q128 Why is it important to split data into training and testing datasets?
To increase dataset size
To evaluate model performance on unseen data
To clean data
To preprocess features
Q129
Q129 Which Python library provides the train_test_split function?
NumPy
Pandas
scikit-learn
Matplotlib
Q130
Q130 How do you train a linear regression model using scikit-learn?
model.fit(X, y)
model.train(X, y)
model.learn(X, y)
model.predict(X, y)
Q131
Q131 Which scikit-learn function is used to calculate the accuracy of a classification model?
classification_report
accuracy_score
score
confusion_matrix
Q132
Q132 A model's predictions have high bias. What could be the likely issue?
Overfitting
Underfitting
Feature scaling
Incorrect testing data
Q133
Q133 A classification model achieves 99% accuracy on the training set but only 60% on the test set. What is the issue?
Overfitting
Underfitting
Data imbalance
Feature scaling
Q134
Q134 After training a regression model, the residuals show a clear pattern. What does this imply?
Model is accurate
Model assumptions are violated
Feature scaling is wrong
Data is balanced
Q135
Q135 What is the key difference between supervised and unsupervised learning?
Supervised uses labeled data, unsupervised does not
Both use labeled data
Both use unlabeled data
Unsupervised requires labels
Q136
Q136 Which of the following is an example of a supervised learning algorithm?
K-Means
Linear Regression
Hierarchical Clustering
PCA
Q137
Q137 Which task is best suited for unsupervised learning?
Predicting house prices
Identifying customer segments
Spam classification
Stock price prediction
Q138
Q138 What metric is commonly used to evaluate a regression model in supervised learning?
Accuracy
Mean Squared Error (MSE)
Precision
Silhouette score
Q139
Q139 Why is clustering considered an unsupervised learning technique?
It requires labeled data
It uses supervised models
It finds patterns in unlabeled data
It predicts outcomes
Q140
Q140 Which Python library provides the KMeans function for clustering?
NumPy
Pandas
scikit-learn
Matplotlib
Q141
Q141 How do you fit a decision tree classifier in scikit-learn?
model.train(X, y)
model.fit(X, y)
model.learn(X, y)
model.split(X, y)
Q142
Q142 Which function in scikit-learn is used to calculate the silhouette score for a clustering model?
silhouette_score()
cluster_score()
clustering_score()
silhouette_metric()
Q143
Q143 How do you specify the number of clusters in the KMeans algorithm using scikit-learn?
KMeans(n_clusters=n)
KMeans(clusters=n)
KMeans(n=n)
KMeans(n_cluster=n)
Q144
Q144 A supervised model performs poorly on unseen data. What is the likely issue?
Data leakage
Underfitting
Incorrect loss function
Missing labels
Q145
Q145 A clustering model produces inconsistent results. What could be the likely cause?
Wrong feature scaling
Labeled data
High accuracy
Balanced dataset
Q146
Q146 After applying KMeans, one cluster has very few data points. What should you consider next?
Increase cluster count
Decrease cluster count
Visualize clusters
Change the algorithm
Q147
Q147 What is the primary goal of feature engineering in machine learning?
Improve model interpretability
Reduce dataset size
Enhance model performance
Avoid overfitting
Q148
Q148 Which technique is commonly used to handle categorical data in feature engineering?
Normalization
One-hot encoding
PCA
Standardization
Q149
Q149 Why is feature scaling important in machine learning?
Reduces model size
Improves convergence during training
Handles missing values
Reduces overfitting
Q150
Q150 What is feature selection?
Adding new features
Choosing the best features
Removing outliers
Scaling data
