input: Write a python function to build and evaluate a linear regression model on a given dataset using scikit-learn.
  assert 'Test Score' in build_and_evaluate_model('data.csv')
  assert isinstance(build_and_evaluate_model('data.csv')['Test Score'], float)
  assert 'Sample Predictions' in build_and_evaluate_model('data.csv')
  assert len(build_and_evaluate_model('data.csv')['Sample Predictions']) == 10
output: def build_and_evaluate_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import mean_squared_error
  
      # Load dataset
      data = pd.read_csv(file_path)
      
      # Assume the last column is the target variable and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create and train the model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Evaluate the model
      y_pred = model.predict(X_test)
      test_score = mean_squared_error(y_test, y_pred)
      
      # Get sample predictions
      sample_predictions = model.predict(X_test[:10]).tolist()
      
      return {
          'Test Score': test_score,
          'Sample Predictions': sample_predictions
      }