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A Quiz On Artificial Intelligence And Machine Learning

10 Questions 0 Attempts 156 Seen
This set of questions covers a range of foundational topics in AI and ML, including supervised and unsupervised learning, model evaluation, and applications in natural language processing and deep learning. These questions are designed to test knowledge across various core concepts in AI/ML, suitable for beginners and those preparing for certification exams in these fields.  Start Test without Login in
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Direction: Supervised learning involves training a model on labeled data.

Q.1. What is the main objective of supervised learning in machine learning?

  1. To predict outcomes for new data
  2. To discover hidden patterns in data
  3. To reduce the dimensionality of data
  4. To generate new data from existing data
  5. None of the above

Direction: Classification tasks involve categorizing data into predefined classes.

Q.2. Which algorithm is commonly used for classification tasks?

  1. K-Means Clustering
  2. Linear Regression
  3. Decision Trees
  4. Apriori Algorithm
  5. DBSCAN

Direction: Overfitting is a common problem when a model learns too much from the training data.

Q.3. What does "overfitting" refer to in machine learning?

  1. A model performing well on both training and test data
  2. A model performing poorly on training data but well on test data
  3. A model performing well on training data but poorly on test data
  4. A model that generalizes well to unseen data
  5. A model that performs equally on all datasets

Direction: Unsupervised learning deals with unlabeled data to find patterns.

Q.4. Which of the following is an example of unsupervised learning?

  1. Support Vector Machines
  2. Principal Component Analysis (PCA)
  3. Random Forest
  4. Naive Bayes
  5. Linear Discriminant Analysis

Direction: Learning rate is a key hyperparameter in training neural networks and other models.

Q.5. What is the purpose of a "learning rate" in gradient descent optimization?

  1. To determine the size of steps taken towards the minimum of a function
  2. To increase the model's complexity
  3. To define the number of iterations
  4. To prevent the model from underfitting
  5. To ensure the model converges to a local maximum

Direction: Tokenization is a fundamental step in text preprocessing.

Q.6. In natural language processing, what does "tokenization" refer to?

  1. Breaking down text into paragraphs
  2. Splitting text into sentences
  3. Breaking text into smaller units like words or phrases
  4. Removing punctuation and stopwords
  5. Translating text into another language

Direction: Evaluation metrics help measure the effectiveness of a model's predictions.

Q.7. Which metric is commonly used to evaluate the performance of a classification model?

  1. Mean Squared Error (MSE)
  2. R-squared
  3. F1 Score
  4. Root Mean Squared Error (RMSE)
  5. Adjusted R-squared

Direction: CNNs are a type of deep learning model particularly effective in certain applications.

Q.8. What is a "convolutional neural network" (CNN) primarily used for?

  1. Time series forecasting
  2. Text generation
  3. Image recognition
  4. Speech synthesis
  5. Reinforcement learning

Direction: An agent interacts with an environment to learn optimal actions.

Q.9. In reinforcement learning, what is an "agent"?

  1. The environment where actions are taken
  2. A set of possible actions
  3. A reward function
  4. An entity that learns and makes decisions
  5. A policy for decision-making

Direction: Regularization techniques help in preventing overfitting.

Q.10. What is "regularization" in machine learning?

  1. Increasing model complexity
  2. Adding noise to the data
  3. Penalizing complex models
  4. Optimizing hyperparameters
  5. Reducing model accuracy
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