Which encoding method converts categorical variables into binary columns?

• A) Normalization
• B) One-Hot Encoding
• C) Label Encoding
• D) Feature Scaling

What is the main purpose of feature scaling?

• A) To increase model complexity
• B) To make all features have similar scales and magnitudes
• C) To improve feature correlation
• D) To remove redundant data

What is the main goal of data preprocessing in machine learning?

• A) To collect raw data from various sources
• B) To clean, transform, and prepare data for model training
• C) To visualize results from a trained model
• D) To increase data storage capacity

Which of the following is a common issue with raw data?

• A) Clean numeric format
• B) Balanced classes
• C) Missing values
• D) Perfect accuracy

Which statement best describes underfitting?

• A) The model is too simple and performs poorly on both training and test data
• B) The model is too complex and performs poorly on test data
• C) The model fits training data perfectly
• D) The model captures noise instead of real patterns

Which type of feature selection depends on labeled data?

• A) Supervised
• B) Semi-supervised
• C) Unsupervised
• D) Reinforcement-based

What does the bias-variance tradeoff represent?

• A) The balance between model simplicity and complexity
• B) The difference between mean and standard deviation
• C) The tradeoff between dataset size and model accuracy
• D) The tradeoff between model depth and learning rate

What is the main purpose of Feature Selection in machine learning?

• A) To normalize and standardize the dataset
• B) To select the most relevant features for model building
• C) To create new features from existing data
• D) To increase the number of features

Which of the following is a sign of underfitting?

• A) High test accuracy and low training accuracy
• B) Low training accuracy and low test accuracy
• C) Equal accuracy on both datasets
• D) High training accuracy and low-test accuracy

Which of the following is NOT an advantage of feature selection?

• A) Reduces overfitting
• B) Enhances interpretability
• C) Increases model complexity
• D) Improves model accuracy

Which of the following is a content-based filtering approach?

• A) Using matrix factorization
• B) Recommending items based on user similarity
• C) Recommending items based on item features and user preferences
• D) Using clustering algorithms only

What are the three main components of input data for a recommender system?

• A) Items data, user ratings, and user preferences
• B) User demographics, user behaviour, and user opinions
• C) User data, user interactions, and user preferences
• D) Items data, user data, and interactions between users and items

Find the Eigen values for the following 2×2 matrix: $% MathType!MTEF!2!1!+- % feaahGart1ev3aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9 % vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x % fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyqaiabg2 % da9maadmaabaqbaeqabiGaaaqaaiaaigdaaeaacaaI4aaabaGaaGOm % aaqaaiaaigdaaaaacaGLBbGaayzxaaaaaa!3CB9! A = \left[ {\begin{array}{*{20}{c}} 1&8\\ 2&1 \end{array}} \right]$

• A) 4
• B) 6
• C) 2
• D) -3

What is the "cold start" problem in recommendation systems?

• A) Slow training of models
• B) System failure due to low temperature
• C) Overfitting of the model
• D) Lack of data for new users or items

Which of the following is NOT a property of the transpose of a matrix?

• A) (kA)’=KA’
• B) (AB)’=(BA)’
• C) A’)’=A
• D) (A+B)’=A’+B’

Which algorithm is most commonly used for recommendation systems?

• A) PCA
• B) Naive Bayes
• C) Random Forest
• D) Collaborative Filtering

What is the goal of a recommender system?

• A) Filter out irrelevant items
• B) Provide users with hints and suggestions about items that can meet their interests
• C) Generate random recommendations
• D) Analyze user data for marketing purposes

How can implicit ratings be collected in a recommender system?

• A) By analyzing user opinions
• B) By analyzing user demographics
• C) By asking users to rate items explicitly
• D) By observing user behavior

In collaborative filtering, recommendations are based on:

• A) Item features only
• B) Random selection
• C) User interaction history and similarities between users/items
• D) Predefined rules

What is the purpose of matrix factorization in recommendation systems?

• A) To decompose the user-item interaction matrix into latent features
• B) To increase dataset size
• C) To remove noise from data
• D) To normalize input values