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NEW QUESTION # 37
What does the term 'reinforcement learning' refer to in machine learning?
Response:
- A. The method of combining multiple models to improve performance
- B. A type of unsupervised learning
- C. The process of training neural networks with backpropagation
- D. A learning technique where an agent learns to make decisions by taking actions in an environment to achieve some reward
Answer: D
NEW QUESTION # 38
What is the difference between simple linear regression and multiple linear regression?
Response:
- A. Simple linear regression uses two dependent variables, while multiple linear regression uses one
- B. Simple linear regression is used for classification, while multiple linear regression is used for clustering
- C. Simple linear regression has one independent variable, while multiple linear regression has two or more independent variables
- D. There is no difference between them
Answer: C
NEW QUESTION # 39
In a fully connected neural network, what is the primary role of the activation function?
Response:
- A. To optimize the learning rate
- B. To reduce overfitting
- C. To compute the error
- D. To introduce non-linearity into the model
Answer: D
NEW QUESTION # 40
What is the Fourier Series used for in data science?
Response:
- A. To calculate the mean of the dataset
- B. To generate random numbers for a model
- C. To break down periodic functions into sums of sine and cosine terms
- D. To determine the probability of an event
Answer: C
NEW QUESTION # 41
What is not a typical use case for CNNs in image processing?
Response:
- A. Natural language processing
- B. Facial recognition
- C. Image classification
- D. Object detection
Answer: A
NEW QUESTION # 42
What does 'grid search' refer to in hyperparameter tuning?
Response:
- A. A strategy for reducing the number of hyperparameters
- B. A method to visualize the performance of different models
- C. The process of randomly selecting hyperparameters for testing
- D. A technique to search exhaustively through a specified subset of hyperparameters
Answer: D
NEW QUESTION # 43
What is the scikit-learn library in Python best used for?
Response:
- A. Large-scale data processing
- B. High-performance computing
- C. Advanced data visualization
- D. Machine learning model development
Answer: D
NEW QUESTION # 44
You are tasked with collecting data from an external API for use in a machine learning model. After making several requests, you notice that the data contains missing values and inconsistencies across different records. What steps should you take to clean and prepare this data for further analysis and modeling?
Response:
- A. Focus on fixing inconsistencies only in the training data, ignoring the test data
- B. Collect additional data from a different source without fixing the original dataset
- C. Ignore the inconsistencies and proceed directly to model training
- D. Identify and remove records with missing values, apply data normalization techniques, and ensure data consistency before model training
Answer: D
NEW QUESTION # 45
Your cybersecurity team is tasked with detecting anomalies in network traffic that may indicate malicious activity. You decide to use an autoencoder for this task. After training the autoencoder on normal network traffic data, you notice that it is not accurately detecting anomalies.
What are the next steps you should take to improve the performance of the autoencoder?
Response:
- A. Train the autoencoder solely on anomalous data to improve its accuracy
- B. Adjust the size of the latent space and apply regularization techniques to reduce overfitting
- C. Increase the complexity of the network by adding more layers and disabling dropout
- D. Retrain the autoencoder with fewer data points and remove regularization techniques
Answer: B
NEW QUESTION # 46
Which of the following is a key step in data acquisition for machine learning models?
Response:
- A. Hyperparameter tuning
- B. Model evaluation
- C. Data cleaning
- D. Model deployment
Answer: C
NEW QUESTION # 47
What role does 'batch size' play in training neural networks?
Response:
- A. It adjusts the learning rate of the model
- B. It sets the total number of training iterations
- C. It specifies the number of training examples used in one iteration
- D. It determines the maximum number of features used in the model
Answer: C
NEW QUESTION # 48
In machine learning, what does 'pruning' a decision tree involve?
Response:
- A. Removing branches to reduce complexity and overfitting
- B. Splitting the tree into multiple smaller trees
- C. Adding more branches to the tree
- D. Increasing the depth of the tree
Answer: A
NEW QUESTION # 49
Which strategies can be applied to improve the anomaly detection performance of an autoencoder?
(Choose two)
Response:
- A. Add regularization techniques such as L1 or L2
- B. Increase the size of the latent space
- C. Reduce the number of hidden layers
- D. Train the autoencoder on both normal and anomalous data
Answer: A,D
NEW QUESTION # 50
Which of the following metrics is commonly used to evaluate the performance of a regression model?
Response:
- A. Mean Squared Error (MSE)
- B. F1 score
- C. Confusion matrix
- D. Precision
Answer: A
NEW QUESTION # 51
What does 'one-hot encoding' do in the preprocessing of categorical data?
Response:
- A. It identifies and removes outliers
- B. It converts categorical variables into binary vectors
- C. It scales all features to a uniform range
- D. It reduces the dimensionality of the data
Answer: B
NEW QUESTION # 52
Which of the following is a data manipulation technique commonly applied to prepare data for machine learning models?
Response:
- A. Normalization
- B. Hyperparameter tuning
- C. Data augmentation
- D. Overfitting
Answer: A
NEW QUESTION # 53
You are analyzing the performance of machine learning models by comparing their error rates. After calculating the mean and standard deviation of the errors for each model, you notice that one model has a high standard deviation compared to the others.
What does this suggest about the model's performance, and what steps can you take to improve it?
Response:
- A. The high standard deviation suggests that the model's errors are inconsistent; you should apply regularization or tune hyperparameters to reduce variability
- B. The model's performance is optimal, and no changes are needed
- C. The mean error is sufficient to evaluate performance, and standard deviation can be ignored
- D. Increase the dataset size without making any changes to the model
Answer: A
NEW QUESTION # 54
Which method is NOT typically used for data acquisition in machine learning?
Response:
- A. Manual data entry
- B. SQL queries
- C. Neural network predictions
- D. Web scraping
Answer: C
NEW QUESTION # 55
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