Q. What does CI/CD stand for in the context of MLOps?
A.
Continuous Integration/Continuous Deployment
B.
Cyclic Integration/Cyclic Deployment
C.
Constant Improvement/Constant Development
D.
Collaborative Integration/Collaborative Deployment
Show solution
Solution
CI/CD stands for Continuous Integration and Continuous Deployment, which are practices used to automate the deployment of machine learning models.
Correct Answer:
A
— Continuous Integration/Continuous Deployment
Learn More →
Q. What does CNN stand for in the context of deep learning?
A.
Convolutional Neural Network
B.
Cyclic Neural Network
C.
Complex Neural Network
D.
Conditional Neural Network
Show solution
Solution
CNN stands for Convolutional Neural Network, which is a class of deep neural networks commonly used for analyzing visual imagery.
Correct Answer:
A
— Convolutional Neural Network
Learn More →
Q. What does common subexpression elimination achieve?
A.
It reduces the number of variables
B.
It eliminates duplicate calculations
C.
It simplifies control flow
D.
It increases the number of function calls
Show solution
Solution
Common subexpression elimination achieves the elimination of duplicate calculations, thus optimizing the code.
Correct Answer:
B
— It eliminates duplicate calculations
Learn More →
Q. What does cross-validation help to prevent?
A.
Overfitting
B.
Underfitting
C.
Data leakage
D.
Bias
Show solution
Solution
Cross-validation helps to prevent overfitting by ensuring that the model performs well on unseen data.
Correct Answer:
A
— Overfitting
Learn More →
Q. What does dead code elimination refer to?
A.
Removing code that is never executed
B.
Optimizing loops
C.
Reducing variable declarations
D.
Simplifying expressions
Show solution
Solution
Dead code elimination refers to removing code that is never executed, which can help reduce the size of the code.
Correct Answer:
A
— Removing code that is never executed
Learn More →
Q. What does HTTPS stand for?
A.
HyperText Transfer Protocol Secure
B.
HyperText Transfer Protocol Standard
C.
HyperText Transfer Protocol Service
D.
HyperText Transfer Protocol Socket
Show solution
Solution
HTTPS stands for HyperText Transfer Protocol Secure, which is the secure version of HTTP that uses encryption.
Correct Answer:
A
— HyperText Transfer Protocol Secure
Learn More →
Q. What does it mean if a linear regression model has a p-value less than 0.05 for a predictor variable?
A.
The predictor is not statistically significant
B.
The predictor is statistically significant
C.
The model is overfitting
D.
The model has high bias
Show solution
Solution
A p-value less than 0.05 indicates that the predictor variable is statistically significant in predicting the dependent variable.
Correct Answer:
B
— The predictor is statistically significant
Learn More →
Q. What does LR stand for in LR parsing?
A.
Left-to-right
B.
Right-to-left
C.
Left-to-right with lookahead
D.
Right-to-left with lookahead
Show solution
Solution
LR parsing stands for Left-to-right parsing with a rightmost derivation in reverse.
Correct Answer:
A
— Left-to-right
Learn More →
Q. What does multicollinearity in linear regression refer to?
A.
High correlation between the dependent variable and independent variables
B.
High correlation among independent variables
C.
Low variance in the dependent variable
D.
Independence of errors
Show solution
Solution
Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, which can affect the stability of coefficient estimates.
Correct Answer:
B
— High correlation among independent variables
Learn More →
Q. What does NAT stand for in networking?
A.
Network Address Translation
B.
Network Access Technology
C.
Network Application Transfer
D.
Network Allocation Table
Show solution
Solution
NAT stands for Network Address Translation, a method used to remap one IP address space into another by modifying network address information.
Correct Answer:
A
— Network Address Translation
Learn More →
Q. What does overfitting refer to in machine learning?
A.
A model that performs well on training data but poorly on unseen data
B.
A model that generalizes well to new data
C.
A model that is too simple for the data
D.
A model that has too few features
Show solution
Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.
Correct Answer:
A
— A model that performs well on training data but poorly on unseen data
Learn More →
Q. What does overfitting refer to in supervised learning?
A.
The model performs well on unseen data
B.
The model is too simple to capture the data patterns
C.
The model learns noise in the training data
D.
The model has high bias
Show solution
Solution
Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.
Correct Answer:
C
— The model learns noise in the training data
Learn More →
Q. What does PCA stand for in the context of feature engineering?
A.
Partial Component Analysis
B.
Principal Component Analysis
C.
Predictive Component Analysis
D.
Probabilistic Component Analysis
Show solution
Solution
PCA stands for Principal Component Analysis, a technique used for dimensionality reduction.
Correct Answer:
B
— Principal Component Analysis
Learn More →
Q. What does precision indicate in a classification task?
A.
The ratio of true positives to the sum of true positives and false negatives
B.
The ratio of true positives to the sum of true positives and false positives
C.
The ratio of true negatives to the sum of true negatives and false positives
D.
The overall correctness of the model
Show solution
Solution
Precision measures the accuracy of positive predictions, calculated as true positives divided by the sum of true positives and false positives.
Correct Answer:
B
— The ratio of true positives to the sum of true positives and false positives
Learn More →
Q. What does precision indicate in a confusion matrix?
A.
The ratio of true positives to the total predicted positives
B.
The ratio of true positives to the total actual positives
C.
The overall correctness of the model
D.
The ability to identify all relevant instances
Show solution
Solution
Precision measures the accuracy of positive predictions, calculated as the number of true positives divided by the sum of true positives and false positives.
Correct Answer:
A
— The ratio of true positives to the total predicted positives
Learn More →
Q. What does pruning refer to in the context of Decision Trees?
A.
Adding more nodes to the tree
B.
Removing nodes to reduce complexity
C.
Increasing the depth of the tree
D.
Changing the splitting criterion
Show solution
Solution
Pruning refers to the process of removing nodes from a Decision Tree to reduce its complexity and prevent overfitting.
Correct Answer:
B
— Removing nodes to reduce complexity
Learn More →
Q. What does R-squared indicate in a linear regression analysis?
A.
The strength of the relationship between variables
B.
The proportion of variance in the dependent variable explained by the independent variables
C.
The average error of predictions
D.
The number of predictors in the model
Show solution
Solution
R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in the model.
Correct Answer:
B
— The proportion of variance in the dependent variable explained by the independent variables
Learn More →
Q. What does R-squared indicate in a linear regression model?
A.
The strength of the relationship between the independent and dependent variables
B.
The proportion of variance in the dependent variable that can be explained by the independent variable(s)
C.
The average error of the predictions
D.
The number of predictors in the model
Show solution
Solution
R-squared measures the proportion of variance in the dependent variable that is explained by the independent variable(s) in the model.
Correct Answer:
B
— The proportion of variance in the dependent variable that can be explained by the independent variable(s)
Learn More →
Q. What does R-squared measure in a linear regression model?
A.
The strength of the relationship between the independent and dependent variables
B.
The average error of the predictions
C.
The number of predictors in the model
D.
The slope of the regression line
Show solution
Solution
R-squared measures the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model.
Correct Answer:
A
— The strength of the relationship between the independent and dependent variables
Learn More →
Q. What does recall measure in a classification model?
A.
The ratio of true positives to the total actual positives
B.
The ratio of true positives to the total predicted positives
C.
The ratio of true negatives to the total actual negatives
D.
The ratio of false negatives to the total actual positives
Show solution
Solution
Recall, also known as sensitivity, measures the ability of a model to find all the relevant cases (true positives).
Correct Answer:
A
— The ratio of true positives to the total actual positives
Learn More →
Q. What does recall measure in a classification task?
A.
The ratio of true positives to the total actual positives
B.
The ratio of true positives to the total predicted positives
C.
The overall accuracy of the model
D.
The number of false negatives
Show solution
Solution
Recall, also known as sensitivity, measures the ability of a model to find all relevant cases (true positives) in the dataset.
Correct Answer:
A
— The ratio of true positives to the total actual positives
Learn More →
Q. What does recursion mean in programming?
A.
A function calling itself
B.
A loop that iterates
C.
A data structure
D.
A variable declaration
Show solution
Solution
Recursion in programming refers to a function that calls itself to solve a problem.
Correct Answer:
A
— A function calling itself
Learn More →
Q. What does RMSE stand for in evaluation metrics?
A.
Root Mean Square Error
B.
Relative Mean Square Error
C.
Root Mean Squared Estimation
D.
Relative Mean Squared Estimation
Show solution
Solution
RMSE stands for Root Mean Square Error, which measures the average magnitude of the errors between predicted and observed values.
Correct Answer:
A
— Root Mean Square Error
Learn More →
Q. What does RMSE stand for in the context of evaluation metrics?
A.
Root Mean Square Error
B.
Relative Mean Square Error
C.
Random Mean Square Error
D.
Root Mean Squared Evaluation
Show solution
Solution
RMSE stands for Root Mean Square Error, which measures the average magnitude of the errors between predicted and observed values.
Correct Answer:
A
— Root Mean Square Error
Learn More →
Q. What does RNN stand for in the context of neural networks?
A.
Recurrent Neural Network
B.
Radial Neural Network
C.
Recursive Neural Network
D.
Regularized Neural Network
Show solution
Solution
RNN stands for Recurrent Neural Network, which is designed to recognize patterns in sequences of data.
Correct Answer:
A
— Recurrent Neural Network
Learn More →
Q. What does ROC AUC measure in a classification model?
A.
The area under the Receiver Operating Characteristic curve
B.
The average precision of the model
C.
The total number of true positives
D.
The mean error of predictions
Show solution
Solution
ROC AUC measures the area under the ROC curve, indicating the model's ability to distinguish between classes.
Correct Answer:
A
— The area under the Receiver Operating Characteristic curve
Learn More →
Q. What does ROC AUC measure?
A.
The area under the Receiver Operating Characteristic curve
B.
The accuracy of the model
C.
The precision of the model
D.
The recall of the model
Show solution
Solution
ROC AUC measures the area under the Receiver Operating Characteristic curve, indicating the model's ability to distinguish between classes.
Correct Answer:
A
— The area under the Receiver Operating Characteristic curve
Learn More →
Q. What does ROC AUC stand for in model evaluation?
A.
Receiver Operating Characteristic Area Under Curve
B.
Regression Output Curve Area Under Control
C.
Randomized Output Classification Area Under Curve
D.
Receiver Output Classification Area Under Control
Show solution
Solution
ROC AUC stands for Receiver Operating Characteristic Area Under Curve, measuring the model's ability to distinguish between classes.
Correct Answer:
A
— Receiver Operating Characteristic Area Under Curve
Learn More →
Q. What does ROC stand for in the context of evaluation metrics?
A.
Receiver Operating Characteristic
B.
Randomized Output Curve
C.
Relative Operating Curve
D.
Receiver Output Classification
Show solution
Solution
ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.
Correct Answer:
A
— Receiver Operating Characteristic
Learn More →
Q. What does ROC stand for in the context of model evaluation?
A.
Receiver Operating Characteristic
B.
Receiver Output Curve
C.
Rate of Classification
D.
Random Output Curve
Show solution
Solution
ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.
Correct Answer:
A
— Receiver Operating Characteristic
Learn More →
Showing 721 to 750 of 3237 (108 Pages)
