Question:1
Which of the following is a subfield of Artificial Intelligence (AI) that focuses on enabling machines to learn from data and improve their performance over time?
A) Virtual Reality
B) Natural Language Processing
C) Machine Learning
D) Robotics
Answer:
C) Machine Learning
Explanation:
Machine Learning is a subfield of Artificial Intelligence that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. It focuses on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. While options A, B, and D are also relevant to AI, Machine Learning specifically addresses the aspect of learning from data, making it the correct answer to this question.
Question 2:
What type of machine learning algorithm is commonly used for classification tasks, where the goal is to assign a label to input data points?
A) Regression
B) Clustering
C) Reinforcement Learning
D) Classification
Answer:
D) Classification
Explanation:
Classification is a type of machine learning algorithm that assigns labels to input data points based on their characteristics. It is widely used in tasks such as spam detection, image recognition, and medical diagnosis.
Question 3:
Which programming language is commonly used for implementing artificial intelligence algorithms and models?
A) Java
B) Python
C) C++
D) Ruby
Answer:
B) Python
Explanation:
Python is a widely used programming language in the field of artificial intelligence due to its simplicity, readability, and a rich set of libraries, such as TensorFlow and PyTorch, that support AI development.
Question 4:
What is the purpose of the activation function in a neural network?
A) Data preprocessing
B) Feature extraction
C) Output normalization
D) Introducing non-linearity
Answer:
D) Introducing non-linearity
Explanation:
The activation function in a neural network introduces non-linearity to the model, allowing it to learn from complex patterns and relationships in the data.
Question 5:
In the context of natural language processing, what does POS tagging stand for?
A) Position of Speech tagging
B) Part of Speech tagging
C) Programming Object Structure tagging
D) Predictive Output Sequence tagging
Answer:
B) Part of Speech tagging
Explanation:
POS tagging is a natural language processing task that involves assigning parts of speech (e.g., noun, verb, adjective) to each word in a sentence.
Question 6:
What is the term for a type of unsupervised learning where the goal is to group similar data points together based on their features?
A) Regression
B) Clustering
C) Classification
D) Reinforcement Learning
Answer:
B) Clustering
Explanation:
Clustering is a type of unsupervised learning that involves grouping similar data points together based on their inherent characteristics.
Question 7:
Which AI technique involves agents learning by interacting with their environment and receiving feedback in the form of rewards or punishments?
A) Supervised Learning
B) Unsupervised Learning
C) Reinforcement Learning
D) Semi-supervised Learning
Answer:
C) Reinforcement Learning
Explanation:
Reinforcement Learning involves agents learning to make decisions by receiving feedback in the form of rewards or punishments based on their actions in an environment.
Question 8:
What is the term for a type of neural network layer that reduces the dimensionality of the input data and extracts important features?
A) Activation layer
B) Pooling layer
C) Fully connected layer
D) Convolutional layer
Answer:
B) Pooling layer
Explanation:
Pooling layers in a neural network reduce the spatial dimensions of the input data and help in extracting important features.
Question 9:
What is the Turing Test in the context of artificial intelligence?
A) Test for machine learning accuracy
B) Test for natural language processing
C) Test for machine consciousness
D) Test for machine-human interaction
Answer:
D) Test for machine-human interaction
Explanation:
The Turing Test is a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human during a conversation.
Question 10:
Which AI application involves the development of systems that can mimic or simulate human-like intelligence and decision-making?
A) Expert Systems
B) Genetic Algorithms
C) Fuzzy Logic
D) Artificial General Intelligence (AGI)
Answer:
D) Artificial General Intelligence (AGI)
Explanation:
Artificial General Intelligence (AGI) refers to the development of AI systems with the ability to perform any intellectual task that a human being can. It aims to simulate human-like intelligence and decision-making across a broad range of domains.
Question 11:
What is the term for the process of adjusting the weights of a neural network during training to minimize the difference between predicted and actual outputs?
A) Backpropagation
B) Gradient Descent
C) Forward Propagation
D) Weight Initialization
Answer:
A) Backpropagation
Explanation:
Backpropagation is the process of iteratively adjusting the weights of a neural network based on the error between predicted and actual outputs, allowing the network to learn from the training data.
Question 12:
Which type of machine learning task involves predicting a continuous numerical value, such as the price of a house?
A) Classification
B) Regression
C) Clustering
D) Reinforcement Learning
Answer:
B) Regression
Explanation:
Regression is a type of machine learning task focused on predicting continuous numerical values, making it suitable for tasks like price prediction.
Question 13:
What is the primary function of an artificial neural network’s hidden layers?
A) Input processing
B) Output generation
C) Feature extraction
D) Data visualization
Answer:
C) Feature extraction
Explanation:
Hidden layers in an artificial neural network are responsible for extracting and learning features from the input data, enabling the network to capture complex relationships.
Question 14:
In the context of natural language processing, what is a synonym for “lemmatization”?
A) Stemming
B) Tokenization
C) Synonymization
D) Normalization
Answer:
D) Normalization
Explanation:
Lemmatization is a form of normalization in natural language processing that involves reducing words to their base or root form.
Question 15:
Which ethical concern is associated with the potential biases present in training data used for machine learning models?
A) Overfitting
B) Underfitting
C) Data Leakage
D) Algorithmic Bias
Answer:
D) Algorithmic Bias
Explanation:
Algorithmic bias refers to the presence of unfair or discriminatory outcomes in machine learning models, often due to biased training data.
Question 16:
What is the purpose of a recurrent neural network (RNN) in the context of machine learning?
A) Image recognition
B) Sequence processing
C) Dimensionality reduction
D) Speech synthesis
Answer:
B) Sequence processing
Explanation:
Recurrent Neural Networks (RNNs) are designed for sequence processing tasks, where the order of data points matters, such as in time series analysis or natural language processing.
Question 17:
Which AI technique involves creating systems that can independently make decisions based on predefined rules and knowledge?
A) Reinforcement Learning
B) Expert Systems
C) Genetic Algorithms
D) Fuzzy Logic
Answer:
B) Expert Systems
Explanation:
Expert Systems involve creating AI systems that can make decisions based on a predefined set of rules and knowledge, often in specific domains of expertise.
Question 18:
What is the term for the process of reducing the complexity of a model to improve its generalization on new, unseen data?
A) Regularization
B) Normalization
C) Optimization
D) Generalization
Answer:
A) Regularization
Explanation:
Regularization is the process of adding a penalty term to the model’s objective function to reduce complexity, preventing overfitting and improving generalization.
Question 19:
Which AI application involves the use of algorithms inspired by the biological processes of natural selection and evolution?
A) Swarm Intelligence
B) Genetic Algorithms
C) Ant Colony Optimization
D) Simulated Annealing
Answer:
B) Genetic Algorithms
Explanation:
Genetic Algorithms are a type of optimization algorithm inspired by the process of natural selection, involving the evolution of solutions over generations.
Question 20:
What is the term for a type of machine learning where the algorithm is provided with a dataset that includes both labeled and unlabeled examples?
A) Supervised Learning
B) Unsupervised Learning
C) Semi-supervised Learning
D) Reinforcement Learning
Answer:
C) Semi-supervised Learning
Explanation:
Semi-supervised learning involves training a machine learning model on a dataset that contains both labeled and unlabeled examples, combining aspects of both supervised and unsupervised learning.
Semi-supervised learning involves training a machine learning model on a dataset that contains both labeled and unlabeled examples, combining aspects of both supervised and unsupervised learning.
Question 31:
What is the primary purpose of the “dropout” technique in neural networks?
A) Feature extraction
B) Model compression
C) Regularization
D) Gradient boosting
Answer:
C) Regularization
Explanation:
The “dropout” technique in neural networks is used for regularization, preventing overfitting by randomly dropping (ignoring) some neurons during training.
Question 32:
In machine learning, what is the term for the process of dividing a dataset into two subsets – one for training the model and the other for evaluating its performance?
A) Feature engineering
B) Cross-validation
C) Data partitioning
D) Test set splitting
Answer:
D) Test set splitting
Explanation:
Test set splitting involves dividing a dataset into a training set and a test set, allowing the model’s performance to be evaluated on unseen data.
Question 33:
Which type of algorithm is used to update the weights of a neural network in an online learning scenario, where the model is updated as new data becomes available?
A) Batch Gradient Descent
B) Stochastic Gradient Descent (SGD)
C) Mini-batch Gradient Descent
D) Adam Optimization
Answer:
B) Stochastic Gradient Descent (SGD)
Explanation:
Stochastic Gradient Descent updates the weights of a neural network after each individual data point, making it suitable for online learning scenarios.
Question 34:
What is the term for the process of making a machine learning model more efficient and compact, suitable for deployment on resource-constrained devices?
A) Model pruning
B) Model quantization
C) Model ensemble
D) Model distillation
Answer:
A) Model pruning
Explanation:
Model pruning involves removing unnecessary weights and connections from a model, making it more efficient and suitable for deployment on resource-constrained devices.
Question 35:
Which type of AI system is designed to imitate the decision-making abilities of a human expert in a specific domain?
A) Expert System
B) Genetic Algorithm
C) Fuzzy Logic System
D) Reinforcement Learning System
Answer:
A) Expert System
Explanation:
Expert Systems are AI systems designed to imitate the decision-making abilities of a human expert in a specific domain, typically using a rule-based approach.
Question 36:
In the context of natural language processing, what is the purpose of a “bag-of-words” representation?
A) Capturing word order
B) Identifying named entities
C) Analyzing sentiment
D) Ignoring word frequency
Answer:
A) Capturing word order
Explanation:
A “bag-of-words” representation ignores word order and focuses on the frequency of words in a document, often used for tasks like text classification.
Question 37:
What is the term for a technique in reinforcement learning where the agent learns by observing and imitating the actions of an expert?
A) Policy Gradient
B) Q-Learning
C) Actor-Critic
D) Imitation Learning
Answer:
D) Imitation Learning
Explanation:
Imitation Learning is a reinforcement learning technique where the agent learns by observing and imitating the actions of an expert, rather than exploring the environment on its own.
Question 38:
Which technique involves combining the predictions of multiple weak learners to create a strong learner?
A) Bagging
B) Boosting
C) Stacking
D) Ensemble Learning
Answer:
B) Boosting
Explanation:
Boosting is a technique in ensemble learning where the predictions of multiple weak learners are combined to create a strong learner, with a focus on correcting errors made by previous models.
Question 39:
What is the primary goal of dimensionality reduction techniques in machine learning?
A) Increasing model complexity
B) Enhancing feature diversity
C) Improving model interpretability
D) Reducing the number of features
Answer:
D) Reducing the number of features
Explanation:
Dimensionality reduction techniques aim to reduce the number of features in a dataset while preserving important information, leading to more efficient and interpretable models.
Question 40:
Which AI technique involves simulating the behavior of a group of individuals in nature to solve complex problems?
A) Swarm Intelligence
B) Genetic Algorithms
C) Ant Colony Optimization
D) Simulated Annealing
Answer:
A) Swarm Intelligence
Explanation:
Swarm Intelligence involves simulating the collective behavior of a group of individuals in nature, such as a flock of birds or a swarm of ants, to solve complex problems.
Swarm Intelligence involves simulating the collective behavior of a group of individuals in nature, such as a flock of birds or a swarm of ants, to solve complex problems.
Question 41:
In the context of neural networks, what is the purpose of the “learning rate” parameter during training?
A) Controlling the speed of convergence
B) Specifying the number of hidden layers
C) Adjusting the number of epochs
D) Defining the activation function
Answer:
A) Controlling the speed of convergence
Explanation:
The learning rate in neural networks controls the step size during optimization and influences the speed at which the model converges to the optimal solution.
Question 42:
What is the term for the process of transforming categorical variables into numerical representations that can be used as input for machine learning models?
A) Labeling
B) Encoding
C) Normalization
D) Feature Scaling
Answer:
B) Encoding
Explanation:
Encoding involves transforming categorical variables into numerical representations, allowing them to be used as input for machine learning models.
Question 43:
In the context of natural language processing, what is the purpose of a “tf-idf” representation for a document?
A) Capturing word order
B) Identifying named entities
C) Measuring word importance
D) Analyzing sentiment
Answer:
C) Measuring word importance
Explanation:
The “tf-idf” (Term Frequency-Inverse Document Frequency) representation in natural language processing measures the importance of words in a document relative to a corpus, considering both frequency and rarity.
Question 44:
Which type of machine learning algorithm is trained on a labeled dataset and can make predictions on new, unseen data?
A) Supervised Learning
B) Unsupervised Learning
C) Semi-supervised Learning
D) Reinforcement Learning
Answer:
A) Supervised Learning
Explanation:
Supervised Learning involves training a model on a labeled dataset, where the input data is paired with corresponding output labels, allowing the model to make predictions on new, unseen data.
Question 45:
What is the primary challenge associated with the “curse of dimensionality” in machine learning?
A) Overfitting
B) Underfitting
C) Increased computational complexity
D) Lack of feature diversity
Answer:
C) Increased computational complexity
Explanation:
The “curse of dimensionality” refers to the increased computational complexity and sparsity of data as the number of features or dimensions in a dataset grows.
Question 46:
Which AI technique involves the use of rules and fuzzy logic to represent and reason about uncertainty and imprecision?
A) Expert Systems
B) Genetic Algorithms
C) Ant Colony Optimization
D) Fuzzy Logic Systems
Answer:
D) Fuzzy Logic Systems
Explanation:
Fuzzy Logic Systems use rules and fuzzy logic to represent and reason about uncertainty and imprecision, making them suitable for handling vague or incomplete information.
Question 47:
What is the term for a technique in machine learning where the model is trained on multiple tasks simultaneously to improve overall performance?
A) Multi-task Learning
B) Transfer Learning
C) Ensemble Learning
D) Reinforcement Learning
Answer:
A) Multi-task Learning
Explanation:
Multi-task Learning involves training a model on multiple tasks simultaneously, leveraging shared knowledge to improve overall performance on each task.
Question 48:
In the context of reinforcement learning, what does the term “exploration-exploitation tradeoff” refer to?
A) Balancing the use of different optimization algorithms
B) Balancing the need for more data with the use of existing data
C) Balancing the need to explore the environment with the use of known information
D) Balancing the tradeoff between model complexity and interpretability
Answer:
C) Balancing the need to explore the environment with the use of known information
Explanation:
The exploration-exploitation tradeoff in reinforcement learning refers to the challenge of balancing the need to explore the environment to discover new information and the use of known information to exploit already learned strategies.
Question 49:
What is the term for a technique that involves creating synthetic examples to balance the class distribution in a dataset?
A) Data Augmentation
B) Data Smoothing
C) Overfitting Correction
D) Class Balancing
Answer:
A) Data Augmentation
Explanation:
Data Augmentation involves creating synthetic examples to balance the class distribution in a dataset, often used to address imbalances and improve model performance.
Question 50:
Which AI application involves creating algorithms that can independently improve their performance over time without human intervention?
A) Artificial General Intelligence (AGI)
B) Machine Learning
C) Autonomous Systems
D) Expert Systems
Answer:
C) Autonomous Systems
Explanation:
Autonomous Systems involve creating algorithms and systems that can independently improve their performance over time without human intervention, adapting to changing environments and conditions.
Question 51:
What is the term for the process of fine-tuning a pre-trained neural network on a specific task using a smaller dataset?
A) Transfer Learning
B) Model Pruning
C) Feature Extraction
D) Hyperparameter Tuning
Answer:
A) Transfer Learning
Explanation:
Transfer Learning involves fine-tuning a pre-trained neural network on a specific task using a smaller dataset, leveraging knowledge gained from a larger dataset.
Question 52:
In the context of reinforcement learning, what is the function of the “reward signal”?
A) Specifying the optimal policy
B) Defining the state space
C) Evaluating the agent’s actions
D) Controlling the exploration rate
Answer:
C) Evaluating the agent’s actions
Explanation:
The reward signal in reinforcement learning is used to evaluate the agent’s actions, guiding it toward learning an optimal policy that maximizes cumulative rewards.
Question 53:
Which type of neural network layer is responsible for capturing long-term dependencies in sequential data, such as time series or natural language?
A) Input layer
B) Hidden layer
C) Output layer
D) Long Short-Term Memory (LSTM) layer
Answer:
D) Long Short-Term Memory (LSTM) layer
Explanation:
LSTM layers in neural networks are specifically designed to capture long-term dependencies in sequential data, making them suitable for tasks involving time series or natural language.
Question 54:
What is the term for a technique that involves adjusting the weights of a neural network to prevent them from becoming too large or too small?
A) Weight Initialization
B) Gradient Clipping
C) Batch Normalization
D) Learning Rate Decay
Answer:
B) Gradient Clipping
Explanation:
Gradient Clipping is a technique in neural networks that involves adjusting the gradients during training to prevent the weights from becoming too large or too small, addressing issues like exploding gradients.
Question 55:
Which AI technique involves creating algorithms that can improve their performance by mimicking the process of natural selection?
A) Genetic Algorithms
B) Swarm Intelligence
C) Reinforcement Learning
D) Expert Systems
Answer:
A) Genetic Algorithms
Explanation:
Genetic Algorithms involve creating algorithms that improve their performance by mimicking the process of natural selection, with solutions evolving over multiple generations.
Question 56:
What is the term for the process of training a machine learning model on one set of data and applying it to another, similar set of data?
A) Transfer Learning
B) Cross-Validation
C) Model Evaluation
D) Model Generalization
Answer:
A) Transfer Learning
Explanation:
Transfer Learning involves training a machine learning model on one set of data and applying it to another, similar set of data, leveraging knowledge gained from the source task.
Question 57:
In the context of natural language processing, what is the purpose of a “stopword” removal step in text preprocessing?
A) Capturing word order
B) Enhancing sentiment analysis
C) Reducing dimensionality
D) Eliminating common words
Answer:
D) Eliminating common words
Explanation:
Stopword removal in natural language processing involves eliminating common words (stopwords) from a text, as they often do not carry significant meaning for the analysis.
Question 58:
What is the term for a technique that involves combining the predictions of multiple models through a voting mechanism to make a final prediction?
A) Bagging
B) Boosting
C) Stacking
D) Ensemble Learning
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves combining the predictions of multiple models through a voting mechanism to make a final prediction, improving overall model performance.
Question 59:
Which type of learning involves training a model with limited labeled data and a large amount of unlabeled data, allowing the model to generalize better to new tasks?
A) Supervised Learning
B) Unsupervised Learning
C) Semi-supervised Learning
D) Reinforcement Learning
Answer:
C) Semi-supervised Learning
Explanation:
Semi-supervised learning involves training a model with limited labeled data and a large amount of unlabeled data, improving generalization to new tasks with limited labeled examples.
Question 60:
In the context of reinforcement learning, what is the term for the policy that selects actions based on the probability distribution over possible actions?
A) Deterministic Policy
B) Stochastic Policy
C) Optimal Policy
D) Exploration Policy
Answer:
B) Stochastic Policy
Explanation:
A Stochastic Policy in reinforcement learning selects actions based on a probability distribution over possible actions, introducing randomness into the decision-making process.
Question 61:
Which term is used to describe the phenomenon where a machine learning model performs well on the training data but fails to generalize to new, unseen data?
A) Overfitting
B) Underfitting
C) Bias
D) Variance
Answer:
A) Overfitting
Explanation:
Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, but struggles to generalize to new, unseen data.
Question 62:
What is the purpose of the “ReLU” activation function in a neural network?
A) Introducing non-linearity
B) Outputting binary values
C) Reducing dimensionality
D) Normalizing the input
Answer:
A) Introducing non-linearity
Explanation:
The Rectified Linear Unit (ReLU) activation function introduces non-linearity to a neural network by outputting the input for positive values and zero for negative values.
Question 63:
Which algorithm is commonly used for optimizing the weights of a neural network during training?
A) Genetic Algorithm
B) Gradient Descent
C) Ant Colony Optimization
D) Simulated Annealing
Answer:
B) Gradient Descent
Explanation:
Gradient Descent is a common algorithm used to optimize the weights of a neural network during training by adjusting them in the direction that minimizes the loss function.
Question 64:
What is the term for the process of reducing the impact of outliers or extreme values in a dataset during data preprocessing?
A) Data Augmentation
B) Feature Scaling
C) Outlier Detection
D) Robust Scaling
Answer:
D) Robust Scaling
Explanation:
Robust Scaling is a data preprocessing technique that reduces the impact of outliers or extreme values by scaling the features based on their interquartile range.
Question 65:
In the context of natural language processing, what does the acronym “NLP” stand for?
A) Natural Language Programming
B) Neural Language Processing
C) Networked Linguistic Processing
D) Natural Language Processing
Answer:
D) Natural Language Processing
Explanation:
NLP stands for Natural Language Processing, which involves the interaction between computers and human language, enabling machines to understand, interpret, and generate human-like text.
Question 66:
Which type of neural network layer is responsible for combining information from different parts of the input in a weighted manner?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Pooling layer
Answer:
C) Fully connected layer
Explanation:
A Fully Connected layer in a neural network is responsible for combining information from different parts of the input in a weighted manner, connecting each neuron to every neuron in the previous layer.
Question 67:
What is the term for a type of ensemble learning where multiple models are trained sequentially, and each model corrects the errors of its predecessor?
A) Bagging
B) Boosting
C) Stacking
D) Ensemble Learning
Answer:
B) Boosting
Explanation:
Boosting is an ensemble learning technique where multiple models are trained sequentially, and each model corrects the errors of its predecessor, leading to improved overall performance.
Question 68:
In the context of natural language processing, what is the purpose of “stemming”?
A) Recognizing named entities
B) Capturing word order
C) Reducing words to their base form
D) Identifying part-of-speech tags
Answer:
C) Reducing words to their base form
Explanation:
Stemming in natural language processing involves reducing words to their base or root form, simplifying variations of words to a common base.
Question 69:
What is the term for a type of machine learning where the algorithm learns to map input data directly to output without the need for explicit labeled examples?
A) Supervised Learning
B) Unsupervised Learning
C) Semi-supervised Learning
D) Self-supervised Learning
Answer:
D) Self-supervised Learning
Explanation:
Self-supervised learning involves a machine learning algorithm learning to map input data directly to output without the need for explicit labeled examples, often by creating its own labels.
Question 70:
What is the primary goal of a convolutional neural network (CNN) in image processing tasks?
A) Capturing long-term dependencies
B) Extracting spatial features
C) Recognizing named entities
D) Enhancing sentiment analysis
Answer:
B) Extracting spatial features
Explanation:
Convolutional Neural Networks (CNNs) in image processing tasks are designed to extract spatial features from input images, enabling them to capture patterns and structures effectively.
Question 71:
What is the primary function of the “softmax” activation function in the output layer of a neural network for multi-class classification?
A) Introducing non-linearity
B) Generating probability distribution
C) Reducing dimensionality
D) Preventing overfitting
Answer:
B) Generating probability distribution
Explanation:
The “softmax” activation function in the output layer of a neural network is used for multi-class classification to generate a probability distribution over different classes, allowing the model to make predictions.
Question 72:
In the context of natural language processing, what is the purpose of “tokenization”?
A) Recognizing named entities
B) Capturing word order
C) Breaking text into individual words or tokens
D) Reducing words to their base form
Answer:
C) Breaking text into individual words or tokens
Explanation:
Tokenization in natural language processing involves breaking text into individual words or tokens, facilitating further analysis and processing.
Question 73:
Which algorithm is commonly used for image classification tasks and is known for its simplicity and effectiveness?
A) Support Vector Machine (SVM)
B) K-Nearest Neighbors (KNN)
C) Decision Tree
D) Random Forest
Answer:
A) Support Vector Machine (SVM)
Explanation:
Support Vector Machines (SVM) are commonly used for image classification tasks due to their simplicity and effectiveness in finding a decision boundary between classes.
Question 74:
What is the term for the process of randomly shuffling and partitioning a dataset into training, validation, and test sets during machine learning model development?
A) Data Augmentation
B) Data Splitting
C) Cross-Validation
D) Bootstrap Sampling
Answer:
B) Data Splitting
Explanation:
Data splitting involves randomly shuffling and partitioning a dataset into training, validation, and test sets to assess and improve the performance of a machine learning model.
Question 75:
Which type of machine learning algorithm is used for clustering similar data points together based on their features?
A) Supervised Learning
B) Unsupervised Learning
C) Semi-supervised Learning
D) Reinforcement Learning
Answer:
B) Unsupervised Learning
Explanation:
Unsupervised Learning involves clustering similar data points together based on their features, without the use of labeled examples.
Question 76:
What is the term for the measure of the amount of uncertainty or disorder in a set of data in the context of decision tree algorithms?
A) Gini Index
B) Information Gain
C) Entropy
D) Splitting Criteria
Answer:
C) Entropy
Explanation:
Entropy is a measure of the amount of uncertainty or disorder in a set of data, commonly used as a splitting criterion in decision tree algorithms.
Question 77:
Which technique involves training multiple models in parallel and combining their predictions to improve overall performance?
A) Bagging
B) Boosting
C) Stacking
D) Ensemble Learning
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves training multiple models in parallel and combining their predictions to improve overall performance and robustness.
Question 78:
What is the term for a technique that involves training a model on diverse datasets and combining their predictions to improve generalization?
A) Transfer Learning
B) Domain Adaptation
C) Federated Learning
D) Ensemble Learning
Answer:
D) Ensemble Learning
Explanation:
Ensemble Learning involves training a model on diverse datasets and combining their predictions to improve generalization and performance.
Question 79:
Which optimization algorithm adapts the learning rate during training to speed up convergence and improve performance?
A) Gradient Descent
B) Adam Optimization
C) Stochastic Gradient Descent (SGD)
D) Mini-batch Gradient Descent
Answer:
B) Adam Optimization
Explanation:
Adam Optimization is an algorithm that adapts the learning rate during training, combining ideas from both Momentum and RMSprop, to speed up convergence and improve performance.
Question 80:
What is the primary purpose of dropout regularization in neural networks?
A) Preventing overfitting
B) Introducing non-linearity
C) Enhancing interpretability
D) Reducing dimensionality
Answer:
A) Preventing overfitting
Explanation:
Dropout regularization in neural networks is used to prevent overfitting by randomly dropping (ignoring) some neurons during training, reducing reliance on specific neurons and improving generalization.
Question 81:
What is the term for a type of unsupervised learning algorithm that aims to find a low-dimensional representation of the input data while preserving its essential structure?
A) Principal Component Analysis (PCA)
B) K-Means Clustering
C) Hierarchical Clustering
D) DBSCAN
Answer:
A) Principal Component Analysis (PCA)
Explanation:
Principal Component Analysis (PCA) is an unsupervised learning algorithm that aims to find a low-dimensional representation of the input data while preserving its essential structure.
Question 82:
In the context of natural language processing, what is the purpose of “lemmatization”?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Recognizing named entities
D) Identifying part-of-speech tags
Answer:
B) Reducing words to their base form
Explanation:
Lemmatization in natural language processing involves reducing words to their base or root form, similar to stemming, but considering the context and producing valid words.
Question 83:
Which type of neural network layer is commonly used for downsampling and reducing the spatial dimensions of the input in image processing tasks?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Pooling layer
Answer:
D) Pooling layer
Explanation:
Pooling layers in neural networks are commonly used for downsampling and reducing the spatial dimensions of the input, often applied in image processing tasks.
Question 84:
What is the primary purpose of the “sigmoid” activation function in the output layer of a neural network for binary classification?
A) Introducing non-linearity
B) Generating probability distribution
C) Reducing dimensionality
D) Ensuring output between 0 and 1
Answer:
D) Ensuring output between 0 and 1
Explanation:
The “sigmoid” activation function in the output layer of a neural network for binary classification ensures that the output is between 0 and 1, representing probabilities.
Question 85:
Which machine learning algorithm is suitable for identifying patterns in data and making predictions without explicit programming?
A) Decision Tree
B) Linear Regression
C) Support Vector Machine (SVM)
D) Neural Network
Answer:
D) Neural Network
Explanation:
Neural networks are machine learning algorithms that can identify patterns in data and make predictions without explicit programming, learning from examples.
Question 86:
What is the term for the process of searching for the best combination of hyperparameters to optimize the performance of a machine learning model?
A) Feature Engineering
B) Model Selection
C) Hyperparameter Tuning
D) Cross-Validation
Answer:
C) Hyperparameter Tuning
Explanation:
Hyperparameter tuning involves searching for the best combination of hyperparameters to optimize the performance of a machine learning model.
Question 87:
In reinforcement learning, what is the term for the environment’s representation of the current situation or state?
A) Policy
B) Action
C) Reward
D) State
Answer:
D) State
Explanation:
In reinforcement learning, the term “state” refers to the environment’s representation of the current situation or condition.
Question 88:
What is the primary goal of the “kernel trick” in support vector machines (SVM)?
A) Enhancing model interpretability
B) Handling imbalanced datasets
C) Transforming data into a higher-dimensional space
D) Reducing computational complexity
Answer:
C) Transforming data into a higher-dimensional space
Explanation:
The “kernel trick” in support vector machines involves transforming data into a higher-dimensional space, making it easier to find a hyperplane that separates different classes.
Question 89:
Which type of reinforcement learning algorithm involves estimating the value function and uses it to make decisions?
A) Model-Free Methods
B) Value-Based Methods
C) Policy-Based Methods
D) Actor-Critic Methods
Answer:
B) Value-Based Methods
Explanation:
Value-Based Methods in reinforcement learning involve estimating the value function and using it to make decisions about which actions to take.
Question 90:
In natural language processing, what is the purpose of “Named Entity Recognition” (NER)?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Identifying and classifying entities in text
D) Recognizing part-of-speech tags
Answer:
C) Identifying and classifying entities in text
Explanation:
Named Entity Recognition (NER) in natural language processing involves identifying and classifying entities, such as names of people, organizations, and locations, in text.
Named Entity Recognition (NER) in natural language processing involves identifying and classifying entities, such as names of people, organizations, and locations, in text.
Question 91:
What is the term for the process of fine-tuning a pre-trained language model on a specific downstream natural language processing task?
A) Feature Extraction
B) Transfer Learning
C) Tokenization
D) Embedding
Answer:
B) Transfer Learning
Explanation:
Transfer learning in natural language processing involves fine-tuning a pre-trained language model on a specific downstream task to leverage knowledge gained from a broader dataset.
Question 92:
In the context of reinforcement learning, what is the term for the value that represents the expected cumulative future rewards for a given state and action?
A) Policy
B) Value Function
C) Reward Function
D) Q-Value
Answer:
D) Q-Value
Explanation:
In reinforcement learning, the Q-Value represents the expected cumulative future rewards for a given state and action.
Question 93:
Which type of machine learning algorithm is commonly used for anomaly detection and identifying data points that deviate from the norm?
A) Clustering
B) Classification
C) Regression
D) One-Class SVM
Answer:
D) One-Class SVM
Explanation:
One-Class Support Vector Machine (SVM) is commonly used for anomaly detection, identifying data points that deviate from the norm.
Question 94:
What is the primary purpose of the “LSTM” (Long Short-Term Memory) layer in recurrent neural networks (RNNs)?
A) Image classification
B) Capturing long-term dependencies in sequential data
C) Feature extraction
D) Text summarization
Answer:
B) Capturing long-term dependencies in sequential data
Explanation:
The LSTM (Long Short-Term Memory) layer in recurrent neural networks is designed to capture long-term dependencies in sequential data, making it suitable for tasks like natural language processing.
Question 95:
In the context of machine learning, what does the term “bias” refer to?
A) The ability of a model to generalize to new data
B) The error introduced by approximating a real-world problem
C) Systematic errors in predictions that are not due to randomness
D) The variability in model predictions
Answer:
C) Systematic errors in predictions that are not due to randomness
Explanation:
In machine learning, bias refers to systematic errors in predictions that are not due to randomness, indicating a model’s tendency to consistently deviate from the true values.
Question 96:
Which type of machine learning model is designed to mimic the structure and function of the human brain with interconnected artificial neurons?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Neural Network
D) K-Nearest Neighbors (KNN)
Answer:
C) Neural Network
Explanation:
Neural networks are machine learning models designed to mimic the structure and function of the human brain with interconnected artificial neurons.
Question 97:
What is the term for the measure of how much the output of a function changes in response to a small change in the input?
A) Slope
B) Variance
C) Gradient
D) Entropy
Answer:
C) Gradient
Explanation:
The gradient is the measure of how much the output of a function changes in response to a small change in the input, indicating the direction of the steepest ascent.
Question 98:
In the context of natural language processing, what is the purpose of “word embeddings”?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Representing words as dense vectors in a continuous space
D) Identifying part-of-speech tags
Answer:
C) Representing words as dense vectors in a continuous space
Explanation:
Word embeddings in natural language processing represent words as dense vectors in a continuous space, capturing semantic relationships between words.
Question 99:
Which technique involves training a model on multiple tasks to improve overall performance, even if the tasks are not directly related?
A) Multi-task Learning
B) Transfer Learning
C) Domain Adaptation
D) Ensemble Learning
Answer:
A) Multi-task Learning
Explanation:
Multi-task learning involves training a model on multiple tasks simultaneously to improve overall performance, even if the tasks are not directly related.
Question 100:
What is the term for a type of neural network architecture that consists of layers of interconnected nodes and is used for regression tasks?
A) Convolutional Neural Network (CNN)
B) Recurrent Neural Network (RNN)
C) Feedforward Neural Network
D) Radial Basis Function Network (RBFN)
Answer:
C) Feedforward Neural Network
Explanation:
A Feedforward Neural Network is a type of neural network architecture that consists of layers of interconnected nodes and is commonly used for regression tasks.
Question 101:
What is the term for the technique in which the weights of a neural network are adjusted based on the error in prediction, with the goal of minimizing the loss function?
A) Gradient Descent
B) Backpropagation
C) Genetic Algorithm
D) Simulated Annealing
Answer:
B) Backpropagation
Explanation:
Backpropagation is the technique in which the weights of a neural network are adjusted based on the error in prediction, with the goal of minimizing the loss function through iterative optimization.
Question 102:
In the context of natural language processing, what is the purpose of the “bag-of-words” representation?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Measuring word importance
Answer:
C) Breaking text into individual words or tokens
Explanation:
The “bag-of-words” representation in natural language processing involves breaking text into individual words or tokens, disregarding word order but capturing word frequency information.
Question 103:
Which type of neural network layer is commonly used for detecting patterns or features in images, such as edges and textures?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Pooling layer
Answer:
A) Convolutional layer
Explanation:
Convolutional layers in neural networks are commonly used for detecting patterns or features in images, such as edges and textures, making them essential for image processing tasks.
Question 104:
What is the primary purpose of the “dropout” regularization technique in neural networks?
A) Preventing overfitting
B) Introducing non-linearity
C) Enhancing interpretability
D) Reducing dimensionality
Answer:
A) Preventing overfitting
Explanation:
Dropout is a regularization technique in neural networks used to prevent overfitting by randomly excluding (dropping out) some neurons during training.
Question 105:
In machine learning, what does the term “unsupervised learning” refer to?
A) Training a model with labeled data
B) Training a model without any data
C) Training a model with limited labeled data
D) Training a model without explicit output labels
Answer:
D) Training a model without explicit output labels
Explanation:
Unsupervised learning involves training a model without explicit output labels, allowing it to discover patterns and relationships in the input data.
Question 106:
What is the term for a type of ensemble learning where multiple models are trained independently, and their predictions are averaged to make a final prediction?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves training multiple models independently, and their predictions are averaged to make a final prediction, leading to improved model performance.
Question 107:
In reinforcement learning, what is the term for the numerical value that represents the preference for taking a specific action in a given state?
A) Policy
B) Value Function
C) Q-Value
D) Reward
Answer:
C) Q-Value
Explanation:
In reinforcement learning, the Q-Value represents the preference for taking a specific action in a given state, indicating the expected cumulative future rewards.
Question 108:
Which algorithm is commonly used for clustering data points based on their similarity?
A) Support Vector Machine (SVM)
B) K-Means Clustering
C) Decision Tree
D) Linear Regression
Answer:
B) K-Means Clustering
Explanation:
K-Means Clustering is a commonly used algorithm for clustering data points based on their similarity, grouping them into K clusters.
Question 109:
In the context of natural language processing, what is the purpose of the “tf-idf” (Term Frequency-Inverse Document Frequency) representation?
A) Measuring word importance
B) Capturing word order
C) Breaking text into individual words or tokens
D) Identifying part-of-speech tags
Answer:
A) Measuring word importance
Explanation:
The “tf-idf” (Term Frequency-Inverse Document Frequency) representation in natural language processing is used to measure the importance of words in a document relative to a corpus.
Question 110:
Which type of machine learning algorithm is suitable for predicting a continuous output, such as the price of a house or the temperature?
A) Classification
B) Regression
C) Clustering
D) Reinforcement Learning
Answer:
B) Regression
Explanation:
Regression algorithms in machine learning are suitable for predicting a continuous output, such as the price of a house or the temperature, based on input features.
Question 111:
What is the term for a technique that involves creating synthetic data points to supplement the original dataset and improve model performance?
A) Feature Engineering
B) Data Augmentation
C) Hyperparameter Tuning
D) Ensemble Learning
Answer:
B) Data Augmentation
Explanation:
Data Augmentation is a technique that involves creating synthetic data points to supplement the original dataset, enhancing model training and performance.
Question 112:
In natural language processing, what is the purpose of the “n-gram” model?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Capturing word order
D) Measuring word importance
Answer:
C) Capturing word order
Explanation:
The “n-gram” model in natural language processing is used to capture word order by considering sequences of consecutive words, aiding in language modeling and text prediction.
Question 113:
Which technique involves training a model on data from one domain and applying it to a different, but related, domain?
A) Transfer Learning
B) Domain Adaptation
C) Multi-task Learning
D) Ensemble Learning
Answer:
B) Domain Adaptation
Explanation:
Domain Adaptation involves training a model on data from one domain and applying it to a different, but related, domain, adapting the model to new circumstances.
Question 114:
What is the primary goal of the “Adam” optimization algorithm in training neural networks?
A) Feature extraction
B) Learning rate adaptation
C) Model interpretability
D) Data augmentation
Answer:
B) Learning rate adaptation
Explanation:
The “Adam” optimization algorithm in training neural networks aims to adapt the learning rate during training, combining ideas from both Momentum and RMSprop to improve convergence.
Question 115:
Which type of machine learning algorithm is used for assigning input data points to predefined categories or classes?
A) Clustering
B) Regression
C) Classification
D) Reinforcement Learning
Answer:
C) Classification
Explanation:
Classification algorithms in machine learning are used for assigning input data points to predefined categories or classes.
Question 116:
What is the term for a type of ensemble learning where multiple models are combined sequentially, with each model correcting the errors of its predecessor?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
B) Boosting
Explanation:
Boosting is an ensemble learning technique where multiple models are combined sequentially, with each model correcting the errors of its predecessor to improve overall performance.
Question 117:
In the context of natural language processing, what is the purpose of “token embeddings” in language models?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Representing words as dense vectors
D) Identifying named entities
Answer:
C) Representing words as dense vectors
Explanation:
Token embeddings in natural language processing involve representing words as dense vectors, capturing semantic relationships between words in a continuous space.
Question 118:
Which type of machine learning model is well-suited for time-series forecasting tasks, such as predicting stock prices or weather conditions?
A) Decision Tree
B) Recurrent Neural Network (RNN)
C) Support Vector Machine (SVM)
D) K-Nearest Neighbors (KNN)
Answer:
B) Recurrent Neural Network (RNN)
Explanation:
Recurrent Neural Networks (RNNs) are well-suited for time-series forecasting tasks, as they can capture temporal dependencies in sequential data.
Question 119:
What is the term for a type of unsupervised learning algorithm that involves grouping similar data points together without predefined categories?
A) Clustering
B) Regression
C) Classification
D) Dimensionality Reduction
Answer:
A) Clustering
Explanation:
Clustering is a type of unsupervised learning algorithm that involves grouping similar data points together without predefined categories or labels.
Question 120:
In reinforcement learning, what is the term for the process of exploring and trying different actions to discover the optimal policy?
A) Exploitation
B) Exploration
C) Policy Iteration
D) Value Iteration
Answer:
B) Exploration
Explanation:
In reinforcement learning, exploration refers to the process of trying different actions to discover the optimal policy, alongside exploitation, which involves choosing the currently best-known action.
Question 121:
What is the term for the technique used to evaluate the performance of a machine learning model on an independent dataset not seen during training?
A) Cross-Validation
B) Data Augmentation
C) Hyperparameter Tuning
D) Model Validation
Answer:
A) Cross-Validation
Explanation:
Cross-Validation is the technique used to evaluate the performance of a machine learning model on an independent dataset not seen during training, providing a more robust assessment.
Question 122:
In the context of natural language processing, what is the purpose of “attention mechanisms” in neural networks?
A) Capturing word order
B) Reducing words to their base form
C) Focusing on relevant parts of input sequences
D) Identifying named entities
Answer:
C) Focusing on relevant parts of input sequences
Explanation:
Attention mechanisms in neural networks are used in natural language processing to focus on relevant parts of input sequences, improving the model’s ability to process information selectively.
Question 123:
Which type of machine learning algorithm is used for estimating a function that maps input data to continuous output values?
A) Classification
B) Regression
C) Clustering
D) Reinforcement Learning
Answer:
B) Regression
Explanation:
Regression algorithms in machine learning are used for estimating a function that maps input data to continuous output values.
Question 124:
What is the term for the phenomenon where a machine learning model is excessively complex and captures noise in the training data?
A) Underfitting
B) Overfitting
C) Bias
D) Variance
Answer:
B) Overfitting
Explanation:
Overfitting is the phenomenon where a machine learning model is excessively complex and captures noise in the training data, leading to poor generalization to new data.
Question 125:
In natural language processing, what is the purpose of the “bagging” technique?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Training multiple models independently and averaging their predictions
D) Measuring word importance
Answer:
C) Training multiple models independently and averaging their predictions
Explanation:
Bagging (Bootstrap Aggregating) in natural language processing involves training multiple models independently and averaging their predictions to improve overall performance.
Question 126:
Which type of neural network layer is commonly used for capturing long-term dependencies in sequential data?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Pooling layer
Answer:
B) Recurrent layer
Explanation:
Recurrent layers in neural networks are commonly used for capturing long-term dependencies in sequential data, making them suitable for tasks like natural language processing.
Question 127:
What is the term for a machine learning technique that involves combining predictions from multiple models to improve overall performance?
A) Feature Engineering
B) Ensemble Learning
C) Transfer Learning
D) Hyperparameter Tuning
Answer:
B) Ensemble Learning
Explanation:
Ensemble Learning is a machine learning technique that involves combining predictions from multiple models to improve overall performance and robustness.
Question 128:
In reinforcement learning, what is the term for the policy that defines the probability distribution over actions given a state?
A) Value Function
B) Q-Value
C) Policy
D) Reward
Answer:
C) Policy
Explanation:
In reinforcement learning, the policy defines the probability distribution over actions given a state, guiding the agent’s decision-making.
Question 129:
Which type of machine learning algorithm is suitable for detecting anomalies or outliers in a dataset?
A) Clustering
B) Regression
C) Classification
D) One-Class SVM
Answer:
D) One-Class SVM
Explanation:
One-Class Support Vector Machine (SVM) is suitable for detecting anomalies or outliers in a dataset without requiring labeled examples.
Question 130:
What is the term for a technique that involves transforming input features into a higher-dimensional space to make them more suitable for learning?
A) Feature Scaling
B) Data Augmentation
C) Kernel Trick
D) Principal Component Analysis (PCA)
Answer:
C) Kernel Trick
Explanation:
The Kernel Trick involves transforming input features into a higher-dimensional space, making them more suitable for learning, often used in algorithms like Support Vector Machines (SVM).
Question 131:
In natural language processing, what is the purpose of the “TF-IDF” (Term Frequency-Inverse Document Frequency) representation?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Measuring word importance
Answer:
D) Measuring word importance
Explanation:
TF-IDF (Term Frequency-Inverse Document Frequency) representation in natural language processing is used to measure the importance of words in a document relative to a corpus.
Question 132:
Which technique involves adjusting the learning rate during training to optimize convergence in optimization algorithms, especially in neural networks?
A) Gradient Descent
B) Stochastic Gradient Descent (SGD)
C) Adam Optimization
D) Mini-batch Gradient Descent
Answer:
C) Adam Optimization
Explanation:
Adam Optimization is a technique that adjusts the learning rate during training to optimize convergence, commonly used in optimization algorithms, especially in neural networks.
Question 133:
What is the term for a type of ensemble learning where multiple models are trained independently, and their predictions are combined using a majority vote for classification tasks?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves training multiple models independently, and their predictions are combined using a majority vote for classification tasks, leading to improved accuracy.
Question 134:
In the context of natural language processing, what is the purpose of “Named Entity Recognition” (NER)?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Identifying and classifying entities in text
D) Recognizing part-of-speech tags
Answer:
C) Identifying and classifying entities in text
Explanation:
Named Entity Recognition (NER) in natural language processing involves identifying and classifying entities, such as names of people, organizations, and locations, in text.
Question 135:
Which type of machine learning model is suitable for handling sequential data and is commonly used in natural language processing tasks like language modeling and text generation?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Recurrent Neural Network (RNN)
D) K-Nearest Neighbors (KNN)
Answer:
C) Recurrent Neural Network (RNN)
Explanation:
Recurrent Neural Networks (RNNs) are suitable for handling sequential data and are commonly used in natural language processing tasks like language modeling and text generation.
Question 136:
What is the term for the measure of how much the output of a function changes with respect to changes in the input?
A) Slope
B) Variance
C) Gradient
D) Entropy
Answer:
C) Gradient
Explanation:
The gradient is the measure of how much the output of a function changes with respect to changes in the input, providing information about the direction of steepest ascent.
Question 137:
Which type of machine learning algorithm is used for grouping similar data points together based on their features without predefined categories?
A) Clustering
B) Regression
C) Classification
D) Dimensionality Reduction
Answer:
A) Clustering
Explanation:
Clustering algorithms in machine learning are used for grouping similar data points together based on their features without predefined categories or labels.
Question 138:
What is the primary purpose of the “ReLU” (Rectified Linear Unit) activation function in neural networks?
A) Generating probability distribution
B) Introducing non-linearity
C) Ensuring output between 0 and 1
D) Capturing long-term dependencies
Answer:
B) Introducing non-linearity
Explanation:
The ReLU (Rectified Linear Unit) activation function in neural networks introduces non-linearity by outputting the input for positive values and zero for negative values, aiding in complex modeling.
Question 139:
In reinforcement learning, what is the term for the approach that combines elements of both value-based and policy-based methods, involving both value function estimation and policy optimization?
A) Model-Free Methods
B) Value-Based Methods
C) Policy-Based Methods
D) Actor-Critic Methods
Answer:
D) Actor-Critic Methods
Explanation:
Actor-Critic Methods in reinforcement learning combine elements of both value-based and policy-based methods, involving both value function estimation and policy optimization.
Question 140:
What is the term for the process of selecting the best features from a dataset to improve model performance and reduce overfitting?
A) Feature Scaling
B) Feature Engineering
C) Feature Extraction
D) Feature Selection
Answer:
D) Feature Selection
Explanation:
Feature Selection is the process of selecting the best features from a dataset to improve model performance, reduce overfitting, and enhance interpretability.
Feature Selection is the process of selecting the best features from a dataset to improve model performance, reduce overfitting, and enhance interpretability.
Question 141:
In natural language processing, what is the purpose of the “Word2Vec” model?
A) Capturing word order
B) Reducing words to their base form
C) Representing words as vectors in a continuous space
D) Identifying and classifying entities in text
Answer:
C) Representing words as vectors in a continuous space
Explanation:
The Word2Vec model in natural language processing is used to represent words as vectors in a continuous space, capturing semantic relationships between words.
Question 142:
Which type of machine learning algorithm is suitable for predicting a binary outcome, such as whether an email is spam or not?
A) Classification
B) Regression
C) Clustering
D) Reinforcement Learning
Answer:
A) Classification
Explanation:
Classification algorithms in machine learning are suitable for predicting binary outcomes, such as whether an email is spam or not.
Question 143:
What is the term for a type of ensemble learning where multiple models are trained sequentially, and each model corrects the errors of its predecessor to improve overall performance?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
B) Boosting
Explanation:
Boosting is an ensemble learning technique where multiple models are trained sequentially, and each model corrects the errors of its predecessor to improve overall performance.
Question 144:
In machine learning, what does the term “bias-variance tradeoff” refer to?
A) The error introduced by approximating a real-world problem
B) The ability of a model to generalize to new data
C) The tradeoff between model complexity and training error
D) Systematic errors in predictions that are not due to randomness
Answer:
C) The tradeoff between model complexity and training error
Explanation:
The bias-variance tradeoff in machine learning refers to the tradeoff between model complexity and training error, highlighting the need to balance bias and variance for optimal model performance.
Question 145:
What is the primary goal of the “k-Nearest Neighbors” (k-NN) algorithm in machine learning?
A) Dimensionality reduction
B) Clustering data points
C) Regression
D) Classification based on similarity to neighbors
Answer:
D) Classification based on similarity to neighbors
Explanation:
The k-Nearest Neighbors (k-NN) algorithm in machine learning is primarily used for classification based on the similarity of a data point to its k nearest neighbors.
Question 146:
Which type of neural network layer is commonly used for summarizing information from previous layers and providing an output in various forms, such as classification probabilities?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Output layer
Answer:
D) Output layer
Explanation:
The output layer in neural networks is commonly used for summarizing information from previous layers and providing an output in various forms, such as classification probabilities.
Question 147:
In reinforcement learning, what is the term for the strategy of exploiting the currently best-known action in a given state?
A) Exploitation
B) Exploration
C) Policy Iteration
D) Value Iteration
Answer:
A) Exploitation
Explanation:
Exploitation in reinforcement learning refers to the strategy of choosing the currently best-known action in a given state, aiming to maximize immediate rewards.
Question 148:
What is the term for the process of transforming categorical variables into a numerical format that can be used as input for machine learning models?
A) Feature Scaling
B) Feature Engineering
C) Feature Extraction
D) One-Hot Encoding
Answer:
D) One-Hot Encoding
Explanation:
One-Hot Encoding is the process of transforming categorical variables into a numerical format that can be used as input for machine learning models.
Question 149:
Which type of machine learning algorithm is well-suited for tasks that involve making a sequence of decisions, such as game-playing or autonomous driving?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Reinforcement Learning
D) K-Nearest Neighbors (KNN)
Answer:
C) Reinforcement Learning
Explanation:
Reinforcement learning is well-suited for tasks that involve making a sequence of decisions, where an agent learns to take actions in an environment to maximize cumulative rewards.
Question 150:
In the context of neural networks, what is the term for the process of updating model weights based on the calculated gradient and the learning rate?
A) Backpropagation
B) Gradient Descent
C) Forward Propagation
D) Stochastic Gradient Descent (SGD)
Answer:
B) Gradient Descent
Explanation:
Gradient Descent is the process in neural networks where model weights are updated based on the calculated gradient and the learning rate, aiming to minimize the loss function during training.
Question 151:
In natural language processing, what is the primary purpose of the “GloVe” (Global Vectors for Word Representation) model?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Capturing word order
D) Representing words as vectors based on global context
Answer:
D) Representing words as vectors based on global context
Explanation:
The GloVe (Global Vectors for Word Representation) model in natural language processing is designed to represent words as vectors based on global context, capturing semantic relationships.
Question 152:
Which type of machine learning algorithm is used for reducing the dimensionality of input data while preserving its important features?
A) Classification
B) Regression
C) Clustering
D) Dimensionality Reduction
Answer:
D) Dimensionality Reduction
Explanation:
Dimensionality Reduction algorithms in machine learning are used for reducing the dimensionality of input data while preserving its important features, aiding in simplifying models.
Question 153:
What is the term for the phenomenon where a machine learning model is too simple and fails to capture the underlying patterns in the training data?
A) Underfitting
B) Overfitting
C) Bias
D) Variance
Answer:
A) Underfitting
Explanation:
Underfitting is the phenomenon where a machine learning model is too simple and fails to capture the underlying patterns in the training data, resulting in poor performance.
Question 154:
In the context of neural networks, what is the term for the technique that involves randomly excluding some neurons during training to prevent overfitting?
A) Dropout
B) Batch Normalization
C) Activation Function
D) Regularization
Answer:
A) Dropout
Explanation:
Dropout is the technique in neural networks that involves randomly excluding some neurons during training to prevent overfitting and improve generalization.
Question 155:
Which type of machine learning model is used for making predictions based on a set of rules or conditions learned from the training data?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Neural Network
D) K-Nearest Neighbors (KNN)
Answer:
A) Decision Tree
Explanation:
Decision Trees are used for making predictions based on a set of rules or conditions learned from the training data, providing interpretable and intuitive decision-making.
Question 156:
What is the term for a type of ensemble learning algorithm that combines predictions from multiple base models through a weighted average or voting mechanism?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
C) Stacking
Explanation:
Stacking is an ensemble learning algorithm that combines predictions from multiple base models through a weighted average or voting mechanism, often leading to improved performance.
Question 157:
In reinforcement learning, what is the term for the process of adjusting the rewards associated with actions to encourage or discourage certain behaviors?
A) Exploration
B) Exploitation
C) Reward Shaping
D) Policy Iteration
Answer:
C) Reward Shaping
Explanation:
Reward Shaping in reinforcement learning involves adjusting the rewards associated with actions to encourage or discourage certain behaviors, influencing the learning process.
Question 158:
Which type of machine learning algorithm is used for estimating a function that maps input data to discrete output values or categories?
A) Regression
B) Classification
C) Clustering
D) Reinforcement Learning
Answer:
B) Classification
Explanation:
Classification algorithms in machine learning are used for estimating a function that maps input data to discrete output values or categories.
Question 159:
What is the term for the measure of how well a machine learning model generalizes to new, unseen data?
A) Training Accuracy
B) Testing Accuracy
C) Validation Accuracy
D) Generalization
Answer:
D) Generalization
Explanation:
Generalization in machine learning refers to how well a model performs on new, unseen data, indicating its ability to make accurate predictions beyond the training set.
Question 160:
In natural language processing, what is the purpose of “stemming” as a text preprocessing technique?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Identifying part-of-speech tags
Answer:
B) Reducing words to their base form
Explanation:
Stemming in natural language processing is a text preprocessing technique used for reducing words to their base or root form, simplifying text analysis and improving model performance.
Question 161:What is the term for the process of selecting the optimal hyperparameters for a machine learning model to achieve better performance?A) Feature SelectionB) Model ValidationC) Hyperparameter TuningD) Cross-ValidationAnswer:C) Hyperparameter TuningExplanation:Hyperparameter Tuning is the process of selecting the optimal hyperparameters for a machine learning model to achieve better performance on the validation or test set.Question 162:In natural language processing, what is the purpose of the “LSTM” (Long Short-Term Memory) layer in neural networks?A) Capturing word orderB) Reducing words to their base formC) Handling long-term dependencies in sequential dataD) Representing words as vectorsAnswer:C) Handling long-term dependencies in sequential dataExplanation:The LSTM (Long Short-Term Memory) layer in neural networks, especially in natural language processing, is designed to handle long-term dependencies in sequential data, improving the model’s ability to capture context.Question 163:Which type of machine learning algorithm is commonly used for identifying patterns and relationships in data without the need for explicit output labels?A) Supervised LearningB) Unsupervised LearningC) Reinforcement LearningD) Semi-Supervised LearningAnswer:B) Unsupervised LearningExplanation:Unsupervised Learning is commonly used for identifying patterns and relationships in data without the need for explicit output labels, allowing the model to discover inherent structures.Question 164:What is the term for a technique that involves generating additional data points by applying various transformations to the existing dataset?A) Feature EngineeringB) Data AugmentationC) Hyperparameter TuningD) Ensemble LearningAnswer:B) Data AugmentationExplanation:Data Augmentation is a technique that involves generating additional data points by applying various transformations to the existing dataset, improving model robustness.Question 165:In the context of machine learning, what does the term “precision” measure?A) The ability of a model to generalize to new dataB) The proportion of true positive predictions among all positive predictionsC) The proportion of true positive predictions among all actual positivesD) The tradeoff between model complexity and training errorAnswer:C) The proportion of true positive predictions among all actual positivesExplanation:Precision in machine learning measures the proportion of true positive predictions among all actual positives, providing insights into the accuracy of positive predictions.Question 166:Which type of neural network architecture is inspired by the structure and function of the human brain, consisting of layers of interconnected nodes?A) Convolutional Neural Network (CNN)B) Recurrent Neural Network (RNN)C) Artificial Neural Network (ANN)D) TransformerAnswer:C) Artificial Neural Network (ANN)Explanation:Artificial Neural Networks (ANNs) are inspired by the structure and function of the human brain, consisting of layers of interconnected nodes, or neurons.Question 167:In machine learning, what is the purpose of the “SVM” (Support Vector Machine) algorithm?A) ClusteringB) RegressionC) ClassificationD) Dimensionality ReductionAnswer:C) ClassificationExplanation:The Support Vector Machine (SVM) algorithm in machine learning is primarily used for classification tasks, separating data points into different classes based on a decision boundary.Question 168:What is the term for the technique used to prevent the model from updating its weights for certain features during training, effectively excluding them from the learning process?A) Feature EngineeringB) Feature SelectionC) Feature ScalingD) Feature MaskingAnswer:B) Feature SelectionExplanation:Feature Selection is the technique used to prevent the model from updating its weights for certain features during training, effectively excluding them from the learning process.Question 169:In reinforcement learning, what is the term for the technique that involves estimating the value function from a set of sampled experiences?A) Policy IterationB) Value IterationC) Monte Carlo MethodD) Q-LearningAnswer:C) Monte Carlo MethodExplanation:Monte Carlo Methods in reinforcement learning involve estimating the value function from a set of sampled experiences, providing a way to update the policy.Question 170:Which type of machine learning algorithm is suitable for predicting a continuous output, such as the stock price or temperature?A) ClassificationB) RegressionC) ClusteringD) Reinforcement LearningAnswer:B) RegressionExplanation:Regression algorithms in machine learning are suitable for predicting a continuous output, such as the stock price or temperature, based on input features.
Question 171:
In natural language processing, what is the primary goal of the “Bag of Words” representation?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Representing words as vectors based on global context
Answer:
C) Breaking text into individual words or tokens
Explanation:
The “Bag of Words” representation in natural language processing is used to break text into individual words or tokens, creating a representation based on word occurrences.
Question 172:
Which type of machine learning algorithm is used for assigning input data points to clusters or groups without predefined categories?
A) Classification
B) Regression
C) Clustering
D) Dimensionality Reduction
Answer:
C) Clustering
Explanation:
Clustering algorithms in machine learning are used for assigning input data points to clusters or groups without predefined categories, based on the similarity of features.
Question 173:
What is the term for the technique that involves transforming input features into a standardized range, often between 0 and 1?
A) Feature Scaling
B) Feature Engineering
C) Feature Extraction
D) Feature Normalization
Answer:
A) Feature Scaling
Explanation:
Feature Scaling is the technique that involves transforming input features into a standardized range, often between 0 and 1, to ensure that all features contribute equally to model training.
Question 174:
In machine learning, what does the term “recall” measure?
A) The ability of a model to generalize to new data
B) The proportion of true positive predictions among all positive predictions
C) The proportion of true positive predictions among all actual positives
D) The tradeoff between model complexity and training error
Answer:
C) The proportion of true positive predictions among all actual positives
Explanation:
Recall in machine learning measures the proportion of true positive predictions among all actual positives, providing insights into the model’s ability to identify all positive instances.
Question 175:
Which type of ensemble learning algorithm builds multiple base models and combines their predictions using a weighted sum or voting mechanism?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
C) Stacking
Explanation:
Stacking is an ensemble learning algorithm that builds multiple base models and combines their predictions using a weighted sum or voting mechanism, often leading to improved performance.
Question 176:
In reinforcement learning, what is the term for the technique that involves updating the action values based on the difference between the observed and predicted rewards?
A) Exploitation
B) Exploration
C) Policy Iteration
D) Q-Learning
Answer:
D) Q-Learning
Explanation:
Q-Learning is a reinforcement learning technique that involves updating the action values based on the difference between the observed and predicted rewards, enabling the agent to learn an optimal policy.
Question 177:
What is the term for a type of unsupervised learning algorithm that involves transforming input features into a lower-dimensional space?
A) Clustering
B) Regression
C) Classification
D) Dimensionality Reduction
Answer:
D) Dimensionality Reduction
Explanation:
Dimensionality Reduction is a type of unsupervised learning algorithm that involves transforming input features into a lower-dimensional space, reducing the complexity of the data.
Question 178:
In natural language processing, what is the purpose of “lemmatization” as a text preprocessing technique?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Identifying part-of-speech tags
Answer:
B) Reducing words to their base form
Explanation:
Lemmatization in natural language processing is a text preprocessing technique used for reducing words to their base or root form, similar to stemming but considering the context of the word.
Question 179:
Which type of machine learning model is well-suited for tasks involving time-series data, such as predicting future stock prices or weather conditions?
A) Decision Tree
B) Recurrent Neural Network (RNN)
C) Support Vector Machine (SVM)
D) K-Nearest Neighbors (KNN)
Answer:
B) Recurrent Neural Network (RNN)
Explanation:
Recurrent Neural Networks (RNNs) are well-suited for tasks involving time-series data, as they can capture temporal dependencies and patterns over time.
Question 180:
What is the term for the technique that involves combining predictions from multiple models by averaging or taking the majority vote, reducing overfitting and improving generalization?
A) Feature Engineering
B) Ensemble Learning
C) Transfer Learning
D) Hyperparameter Tuning
Answer:
B) Ensemble Learning
Explanation:
Ensemble Learning is the technique that involves combining predictions from multiple models by averaging or taking the majority vote, reducing overfitting and improving generalization.
Question 181:
In natural language processing, what is the primary purpose of the “N-gram” model?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Representing words as vectors based on global context
Answer:
A) Capturing word order
Explanation:
The N-gram model in natural language processing is used for capturing word order by considering sequences of words of length N, providing insights into the context of the text.
Question 182:
Which type of machine learning algorithm is commonly used for recommending items to users based on their preferences and behavior?
A) Clustering
B) Regression
C) Recommendation Systems
D) Decision Tree
Answer:
C) Recommendation Systems
Explanation:
Recommendation Systems are commonly used for recommending items to users based on their preferences and behavior, often employed in applications like movie recommendations and online shopping.
Question 183:
What is the term for the technique that involves transforming input features into a representation that retains the most important information while discarding less relevant details?
A) Feature Scaling
B) Feature Engineering
C) Feature Extraction
D) Feature Selection
Answer:
C) Feature Extraction
Explanation:
Feature Extraction is the technique that involves transforming input features into a representation that retains the most important information while discarding less relevant details, aiding in dimensionality reduction.
Question 184:
In machine learning, what does the term “F1 score” measure?
A) Precision and Recall
B) Accuracy
C) Mean Squared Error
D) Area Under the ROC Curve (AUC-ROC)
Answer:
A) Precision and Recall
Explanation:
The F1 score in machine learning is a metric that combines precision and recall, providing a balance between the ability to identify positive instances and the accuracy of positive predictions.
Question 185:
Which type of ensemble learning algorithm builds multiple base models sequentially, with each model correcting the errors of its predecessor to improve overall performance?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
B) Boosting
Explanation:
Boosting is an ensemble learning algorithm that builds multiple base models sequentially, with each model correcting the errors of its predecessor to improve overall performance.
Question 186:
In reinforcement learning, what is the term for the measure of the expected cumulative rewards an agent receives over time following a certain policy?
A) Value Function
B) Q-Value
C) Policy
D) Reward
Answer:
A) Value Function
Explanation:
The Value Function in reinforcement learning is the measure of the expected cumulative rewards an agent receives over time following a certain policy.
Question 187:
What is the term for the technique that involves updating the model weights during training based on the calculated gradient and a portion of the training data?
A) Backpropagation
B) Gradient Descent
C) Stochastic Gradient Descent (SGD)
D) Mini-batch Gradient Descent
Answer:
C) Stochastic Gradient Descent (SGD)
Explanation:
Stochastic Gradient Descent (SGD) is the technique that involves updating the model weights during training based on the calculated gradient and a portion of the training data, improving efficiency.
Question 188:
In machine learning, what is the purpose of the “K-Means” algorithm?
A) Classification
B) Regression
C) Clustering
D) Dimensionality Reduction
Answer:
C) Clustering
Explanation:
The K-Means algorithm in machine learning is used for clustering, grouping data points into K clusters based on their similarity.
Question 189:
What is the term for the process of introducing randomness into the training process to prevent the model from memorizing the training data?
A) Dropout
B) Regularization
C) Overfitting
D) Cross-Validation
Answer:
A) Dropout
Explanation:
Dropout is the process of introducing randomness into the training process to prevent the model from memorizing the training data, reducing overfitting.
Question 190:
Which type of machine learning model is suitable for handling tasks involving both numerical and categorical input features?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Random Forest
D) Gradient Boosting Machine
Answer:
C) Random Forest
Explanation:
Random Forest is suitable for handling tasks involving both numerical and categorical input features, making it a versatile and powerful algorithm.
Question 191:
In natural language processing, what is the purpose of the “Term Frequency-Inverse Document Frequency” (TF-IDF) representation?
A) Capturing word order
B) Reducing words to their base form
C) Breaking text into individual words or tokens
D) Measuring word importance
Answer:
D) Measuring word importance
Explanation:
The TF-IDF (Term Frequency-Inverse Document Frequency) representation in natural language processing is used to measure the importance of words in a document relative to a corpus.
Question 192:
Which technique involves adjusting the learning rate during training to optimize convergence in optimization algorithms, especially in neural networks?
A) Gradient Descent
B) Stochastic Gradient Descent (SGD)
C) Adam Optimization
D) Mini-batch Gradient Descent
Answer:
C) Adam Optimization
Explanation:
Adam Optimization is a technique that adjusts the learning rate during training to optimize convergence, commonly used in optimization algorithms, especially in neural networks.
Question 193:
What is the term for a type of ensemble learning where multiple models are trained independently, and their predictions are combined using a majority vote for classification tasks?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves training multiple models independently, and their predictions are combined using a majority vote for classification tasks, leading to improved accuracy.
Question 194:
In the context of natural language processing, what is the purpose of “Named Entity Recognition” (NER)?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Identifying and classifying entities in text
D) Recognizing part-of-speech tags
Answer:
C) Identifying and classifying entities in text
Explanation:
Named Entity Recognition (NER) in natural language processing involves identifying and classifying entities, such as names of people, organizations, and locations, in text.
Question 195:
Which type of machine learning model is suitable for handling sequential data and is commonly used in natural language processing tasks like language modeling and text generation?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Recurrent Neural Network (RNN)
D) K-Nearest Neighbors (KNN)
Answer:
C) Recurrent Neural Network (RNN)
Explanation:
Recurrent Neural Networks (RNNs) are suitable for handling sequential data and are commonly used in natural language processing tasks like language modeling and text generation.
Question 196:
What is the term for the measure of how much the output of a function changes with respect to changes in the input?
A) Slope
B) Variance
C) Gradient
D) Entropy
Answer:
C) Gradient
Explanation:
The gradient is the measure of how much the output of a function changes with respect to changes in the input, providing information about the direction of steepest ascent.
Question 197:
Which type of machine learning algorithm is used for grouping similar data points together based on their features without predefined categories?
A) Clustering
B) Regression
C) Classification
D) Dimensionality Reduction
Answer:
A) Clustering
Explanation:
Clustering algorithms in machine learning are used for grouping similar data points together based on their features without predefined categories or labels.
Question 198:
What is the primary purpose of the “ReLU” (Rectified Linear Unit) activation function in neural networks?
A) Generating probability distribution
B) Introducing non-linearity
C) Ensuring output between 0 and 1
D) Capturing long-term dependencies
Answer:
B) Introducing non-linearity
Explanation:
The ReLU (Rectified Linear Unit) activation function in neural networks introduces non-linearity by outputting the input for positive values and zero for negative values, aiding in complex modeling.
Question 199:
In reinforcement learning, what is the term for the approach that combines elements of both value-based and policy-based methods, involving both value function estimation and policy optimization?
A) Model-Free Methods
B) Value-Based Methods
C) Policy-Based Methods
D) Actor-Critic Methods
Answer:
D) Actor-Critic Methods
Explanation:
Actor-Critic Methods in reinforcement learning combine elements of both value-based and policy-based methods, involving both value function estimation and policy optimization.
Question 200:What is the term for the process of selecting the best features from a dataset to improve model performance and reduce overfitting?A) Feature ScalingB) Feature EngineeringC) Feature ExtractionD) Feature SelectionAnswer:D) Feature SelectionExplanation:Feature Selection is the process of selecting the best features from a dataset to improve model performance, reduce overfitting, and enhance interpretability.
Question 201:
In natural language processing, what is the purpose of the “Word Embeddings” technique?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Capturing word order
D) Representing words as vectors with semantic meaning
Answer:
D) Representing words as vectors with semantic meaning
Explanation:
Word Embeddings in natural language processing is a technique used for representing words as vectors with semantic meaning, capturing relationships and context between words.
Question 202:
Which type of machine learning algorithm is commonly used for reducing the dimensionality of input data by projecting it onto a lower-dimensional subspace?
A) Decision Tree
B) Principal Component Analysis (PCA)
C) Support Vector Machine (SVM)
D) Random Forest
Answer:
B) Principal Component Analysis (PCA)
Explanation:
Principal Component Analysis (PCA) is a machine learning algorithm commonly used for reducing the dimensionality of input data by projecting it onto a lower-dimensional subspace while preserving important information.
Question 203:
What is the term for the technique that involves training a model on a large dataset and fine-tuning it on a smaller dataset relevant to the specific task?
A) Transfer Learning
B) Ensemble Learning
C) Active Learning
D) Self-Supervised Learning
Answer:
A) Transfer Learning
Explanation:
Transfer Learning is the technique that involves training a model on a large dataset and fine-tuning it on a smaller dataset relevant to the specific task, leveraging knowledge gained from the larger dataset.
Question 204:
In machine learning, what does the term “AUC-ROC” measure?
A) Precision and Recall
B) Accuracy
C) Area under the Receiver Operating Characteristic curve
D) Mean Squared Error
Answer:
C) Area under the Receiver Operating Characteristic curve
Explanation:
AUC-ROC (Area under the Receiver Operating Characteristic curve) in machine learning is a metric that measures the area under the curve plotted by the true positive rate against the false positive rate.
Question 205:
Which type of ensemble learning algorithm builds multiple base models independently and combines their predictions through a voting mechanism for regression tasks?
A) Bagging
B) Boosting
C) Stacking
D) Random Forest
Answer:
A) Bagging
Explanation:
Bagging (Bootstrap Aggregating) involves building multiple base models independently and combining their predictions through a voting mechanism, commonly used for regression tasks.
Question 206:
In reinforcement learning, what is the term for the technique that involves estimating the value of a state by considering the expected cumulative rewards from that state onward?
A) Policy Iteration
B) Value Iteration
C) Monte Carlo Method
D) Q-Learning
Answer:
C) Monte Carlo Method
Explanation:
Monte Carlo Methods in reinforcement learning involve estimating the value of a state by considering the expected cumulative rewards from that state onward, providing a way to update the policy.
Question 207:
What is the term for a type of neural network layer designed to handle sequential data, preserving information from previous time steps?
A) Convolutional layer
B) Recurrent layer
C) Fully connected layer
D) Output layer
Answer:
B) Recurrent layer
Explanation:
A Recurrent layer in neural networks is designed to handle sequential data, preserving information from previous time steps, enabling the model to capture temporal dependencies.
Question 208:
In machine learning, what is the term for the technique that involves generating synthetic data points to balance the class distribution in imbalanced datasets?
A) Feature Scaling
B) Data Augmentation
C) SMOTE (Synthetic Minority Over-sampling Technique)
D) Ensemble Learning
Answer:
C) SMOTE (Synthetic Minority Over-sampling Technique)
Explanation:
SMOTE (Synthetic Minority Over-sampling Technique) is a technique in machine learning that involves generating synthetic data points to balance the class distribution, especially in imbalanced datasets.
Question 209:
In natural language processing, what is the purpose of “tf-idf weighting” as a text preprocessing technique?
A) Breaking text into individual words or tokens
B) Reducing words to their base form
C) Measuring word importance in a document relative to a corpus
D) Identifying and classifying entities in text
Answer:
C) Measuring word importance in a document relative to a corpus
Explanation:
tf-idf weighting in natural language processing is a text preprocessing technique used for measuring word importance in a document relative to a corpus, considering term frequency and inverse document frequency.
Question 210:
Which type of machine learning model is well-suited for handling tasks involving a set of rules and logical conditions, making decisions based on a set of features?
A) Decision Tree
B) Support Vector Machine (SVM)
C) Recurrent Neural Network (RNN)
D) K-Nearest Neighbors (KNN)
Answer:
A) Decision Tree
Explanation:
Decision Trees are well-suited for handling tasks involving a set of rules and logical conditions, making decisions based on a set of features in a hierarchical manner.