Q. What is the role of the hyperplane in SVM?
A.
To cluster the data points
B.
To separate different classes
C.
To reduce dimensionality
D.
To calculate the loss function
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Solution
The hyperplane in SVM serves to separate different classes in the feature space.
Correct Answer:
B
— To separate different classes
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Q. What is the role of the input gate in an LSTM?
A.
To control the flow of information into the cell state.
B.
To output the final prediction.
C.
To determine what information to forget.
D.
To initialize the hidden state.
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Solution
The input gate in an LSTM controls the flow of new information into the cell state.
Correct Answer:
A
— To control the flow of information into the cell state.
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Q. What is the role of the intercept in a linear regression equation?
A.
It represents the slope of the line
B.
It is the predicted value when all predictors are zero
C.
It indicates the strength of the relationship
D.
It is not relevant in linear regression
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Solution
The intercept in a linear regression equation represents the predicted value of the dependent variable when all independent variables are zero.
Correct Answer:
B
— It is the predicted value when all predictors are zero
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Q. What is the role of the kernel function in Support Vector Machines?
A.
To reduce dimensionality
B.
To transform data into a higher-dimensional space
C.
To increase the size of the dataset
D.
To visualize the data
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Solution
The kernel function allows Support Vector Machines to operate in a higher-dimensional space, enabling them to find non-linear decision boundaries.
Correct Answer:
B
— To transform data into a higher-dimensional space
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Q. What is the role of the kernel function in SVM?
A.
To increase the number of features
B.
To transform data into a higher-dimensional space
C.
To reduce overfitting
D.
To normalize the data
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Solution
The kernel function allows SVM to operate in a higher-dimensional space, enabling it to find non-linear decision boundaries.
Correct Answer:
B
— To transform data into a higher-dimensional space
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Q. What is the role of the loss function in a neural network?
A.
To measure the accuracy of predictions
B.
To calculate the gradients for backpropagation
C.
To initialize the weights
D.
To determine the architecture of the network
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Solution
The loss function quantifies how well the neural network's predictions match the actual target values, guiding weight updates.
Correct Answer:
B
— To calculate the gradients for backpropagation
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Q. What is the role of the loss function in supervised learning?
A.
To measure the accuracy of the model
B.
To quantify the difference between predicted and actual values
C.
To optimize the model's parameters
D.
To select features for the model
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Solution
The loss function quantifies the difference between predicted and actual values, guiding the optimization of the model during training.
Correct Answer:
B
— To quantify the difference between predicted and actual values
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Q. What is the role of the loss function in training a neural network?
A.
To measure the accuracy of predictions
B.
To calculate the gradient for backpropagation
C.
To determine the optimal learning rate
D.
To initialize the weights
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Solution
The loss function quantifies how well the neural network's predictions match the actual target values, guiding the training process.
Correct Answer:
B
— To calculate the gradient for backpropagation
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Q. What is the role of the optimizer in training a neural network?
A.
To select the activation function
B.
To adjust the weights based on the loss function
C.
To determine the architecture of the network
D.
To preprocess the input data
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Solution
The optimizer adjusts the weights of the network based on the gradients calculated from the loss function.
Correct Answer:
B
— To adjust the weights based on the loss function
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Q. What is the role of the output layer in a neural network?
A.
To process input data
B.
To extract features
C.
To produce the final predictions
D.
To apply regularization
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Solution
The output layer produces the final predictions of the neural network based on the learned features.
Correct Answer:
C
— To produce the final predictions
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Q. What is the role of the parsing table in an LR parser?
A.
To store the grammar rules.
B.
To determine the next action based on the current state and input symbol.
C.
To keep track of the parse tree.
D.
To manage memory allocation.
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Solution
The parsing table in an LR parser is used to determine the next action (shift, reduce, accept, or error) based on the current state and the next input symbol.
Correct Answer:
B
— To determine the next action based on the current state and input symbol.
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Q. What is the role of the regularization parameter 'C' in SVM?
A.
To control the complexity of the model
B.
To determine the type of kernel used
C.
To set the number of support vectors
D.
To adjust the learning rate
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Solution
The regularization parameter 'C' controls the trade-off between maximizing the margin and minimizing the classification error.
Correct Answer:
A
— To control the complexity of the model
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Q. What is the role of the soft margin in SVM?
A.
To allow some misclassification for better generalization
B.
To ensure all data points are classified correctly
C.
To increase the number of support vectors
D.
To reduce the computational complexity
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Solution
The soft margin allows for some misclassification, which helps improve the model's generalization to unseen data.
Correct Answer:
A
— To allow some misclassification for better generalization
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Q. What is the role of version control in model deployment?
A.
To track changes in model architecture
B.
To manage different datasets
C.
To ensure reproducibility and rollback capabilities
D.
To optimize model performance
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Solution
Version control in model deployment helps ensure reproducibility and provides rollback capabilities in case a newly deployed model performs poorly.
Correct Answer:
C
— To ensure reproducibility and rollback capabilities
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Q. What is the significance of 'feature store' in model deployment?
A.
To store raw model outputs
B.
To manage and serve features for model training and inference
C.
To visualize feature importance
D.
To automate model retraining
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Solution
A feature store centralizes the management of features, making them easily accessible for both training and inference.
Correct Answer:
B
— To manage and serve features for model training and inference
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Q. What is the significance of 'latency' in model deployment?
A.
It measures the model's accuracy
B.
It indicates the time taken to make predictions
C.
It refers to the amount of data processed
D.
It assesses the model's complexity
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Solution
Latency refers to the time taken for the model to process input data and return predictions, which is critical for user experience.
Correct Answer:
B
— It indicates the time taken to make predictions
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Q. What is the significance of containerization in model deployment?
A.
It improves model accuracy
B.
It simplifies the deployment process and ensures consistency
C.
It reduces the need for data preprocessing
D.
It eliminates the need for model monitoring
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Solution
Containerization simplifies the deployment process by packaging the model and its dependencies, ensuring consistency across different environments.
Correct Answer:
B
— It simplifies the deployment process and ensures consistency
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Q. What is the significance of feature engineering in the context of model deployment?
A.
It is only important during model training
B.
It helps in improving model interpretability
C.
It ensures the model can handle new data effectively
D.
It is irrelevant to model performance
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Solution
Feature engineering is crucial for ensuring that the model can effectively handle new data it encounters in production.
Correct Answer:
C
— It ensures the model can handle new data effectively
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Q. What is the significance of the AUC in ROC analysis?
A.
It measures the model's training time
B.
It indicates the model's accuracy
C.
It represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance
D.
It shows the number of features used in the model
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Solution
The AUC (Area Under the Curve) quantifies the overall ability of the model to discriminate between positive and negative classes.
Correct Answer:
C
— It represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance
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Q. What is the significance of the confusion matrix in model evaluation?
A.
It shows the distribution of data
B.
It summarizes the performance of a classification model
C.
It calculates the mean error
D.
It visualizes the training process
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Solution
The confusion matrix summarizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.
Correct Answer:
B
— It summarizes the performance of a classification model
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Q. What is the significance of the learning rate in training neural networks?
A.
It determines the number of layers
B.
It controls how much to change the model in response to the estimated error
C.
It sets the number of epochs
D.
It defines the architecture of the network
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Solution
The learning rate controls how much to change the model's weights in response to the estimated error, influencing the convergence speed and stability.
Correct Answer:
B
— It controls how much to change the model in response to the estimated error
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Q. What is the significance of version control in model deployment?
A.
To track changes in the model and its performance
B.
To improve model training speed
C.
To enhance data preprocessing
D.
To reduce model complexity
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Solution
Version control is significant in model deployment as it allows teams to track changes in the model and its performance over time, facilitating better management and rollback if necessary.
Correct Answer:
A
— To track changes in the model and its performance
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Q. What is the significance of versioning in model deployment?
A.
To keep track of different model architectures
B.
To manage updates and changes to models over time
C.
To ensure data consistency
D.
To improve model accuracy
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Solution
Versioning is important in model deployment to manage updates and changes, ensuring that the correct model is used in production.
Correct Answer:
B
— To manage updates and changes to models over time
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Q. What is the size of a pointer on a 64-bit system?
A.
2 bytes
B.
4 bytes
C.
8 bytes
D.
16 bytes
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Solution
On a 64-bit system, the size of a pointer is typically 8 bytes, as it needs to store a 64-bit memory address.
Correct Answer:
C
— 8 bytes
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Q. What is the space complexity of a breadth-first traversal of a binary tree?
A.
O(n)
B.
O(log n)
C.
O(1)
D.
O(n log n)
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Solution
The space complexity of a breadth-first traversal is O(n) because it needs to store all nodes at the current level in the queue.
Correct Answer:
A
— O(n)
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Q. What is the space complexity of a linked list with n nodes?
A.
O(1)
B.
O(n)
C.
O(n log n)
D.
O(n^2)
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Solution
The space complexity of a linked list with n nodes is O(n) because each node requires space.
Correct Answer:
B
— O(n)
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Q. What is the space complexity of a linked list?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
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Solution
A linked list requires space for each node, which is proportional to the number of elements, leading to a space complexity of O(n).
Correct Answer:
B
— O(n)
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Q. What is the space complexity of a queue implemented using a linked list?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
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Solution
The space complexity of a queue implemented using a linked list is O(n), where n is the number of elements in the queue.
Correct Answer:
B
— O(n)
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Q. What is the space complexity of a queue implemented using two stacks?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
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Solution
The space complexity of a queue implemented using two stacks is O(n) as it requires space for all elements in the queue.
Correct Answer:
B
— O(n)
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Q. What is the space complexity of a recursive binary tree traversal?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
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Solution
The space complexity of a recursive binary tree traversal is O(n) due to the call stack.
Correct Answer:
B
— O(n)
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