input: Write a python function to load a CSV file, split the data into training and test sets, vectorize the text data using CountVectorizer, train a Multinomial Naive Bayes model, and make predictions. 
  assert isinstance(make_predictions('path/to/data', 'text', 'label'), np.ndarray)
  assert len(make_predictions('path/to/data', 'text', 'label')) == len(pd.read_csv('path/to/data')['text'])
  assert make_predictions('path/to/data', 'text', 'label').dtype == int
output: def make_predictions(file_path, text_column, label_column):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(df[text_column], df[label_column], test_size=0.2, random_state=42)
      
      # Vectorize the text data using CountVectorizer
      vectorizer = CountVectorizer()
      X_train_vectorized = vectorizer.fit_transform(X_train)
      X_test_vectorized = vectorizer.transform(X_test)
      
      # Train a Multinomial Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train_vectorized, y_train)
      
      # Make predictions
      predictions = model.predict(X_test_vectorized)
      
      return predictions