GIAC Machine Learning Engineer - GMLE Exam Practice Test

A. Scraping only structured data

B. Ensuring data is collected regularly and follows copyright laws

C. Avoiding the use of any API

D. Collecting as much data as possible without validation

A. A type of unsupervised learning

B. The process of training neural networks with backpropagation

C. The method of combining multiple models to improve performance

D. A learning technique where an agent learns to make decisions by taking actions in an environment to achieve some reward

A. To increase data storage efficiency

B. To improve the accuracy of predictions

C. To reduce the computational complexity

D. To enhance data visualization techniques

A. To reduce the number of input variables, simplifying the model

B. To improve the model's accuracy on training data

C. To increase the computational speed of model training

D. To enhance the feature importance analysis

A. Determining the accuracy of a model

B. Understanding relationships between pairs of variables

C. Identifying missing data

D. Visualizing geographical data

A. To enhance the visualization of the model's performance

B. To speed up the training process

C. To increase the number of features in the model

D. To add constraints to the model to reduce overfitting

A. Using a fixed learning rate throughout the training process

B. Updating model parameters after evaluating each data point

C. Eliminating the need for a learning rate

D. Updating model parameters after evaluating the entire dataset

A. A method used to select the best hyperparameters for a model

B. A process that reduces the dimensionality of the dataset

C. A technique that reduces overfitting by using a combination of decision trees trained on different subsets of the data

D. A clustering algorithm that groups similar data points

A. The standard deviation of the residuals

B. The average of the absolute errors

C. The variance explained by the model

D. The square root of the average of squared differences between prediction and actual observa

A. L1 optimizes the learning rate; L2 optimizes the batch size

B. L1 is used for regression; L2 is used for classification

C. L1 adds the absolute value of coefficients; L2 adds the square of coefficients

D. L1 is for neural networks; L2 is for decision trees

A. Manual calculations

B. APIs

C. Web scraping

D. Model ensembling