AI/ML Fundamentals Quiz - Competitive Exams AWS_AI_Practitioner

Free practice quiz: AI/ML Fundamentals (Competitive Exams AWS_AI_Practitioner). 70 multiple-choice questions with answers and explanations. Play online, no sign-up needed.

This Competitive Exams AWS_AI_Practitioner practice quiz has 70 multiple-choice questions on AI/ML Fundamentals. Try the 5 sample questions below (tap an option to check yourself), then play the full quiz online. No sign-up needed.

Sample questions

1. Which type of machine learning algorithm learns from labeled training data to make predictions?

   1. Unsupervised learning
   2. Reinforcement learning
   3. Self-supervised learning
   4. Supervised learning
   Show answer

   Answer: D. Supervised learning

2. What does 'backpropagation' refer to in neural network training?

   1. The technique of pruning unnecessary neurons
   2. The process of propagating errors backward through the network to update weights
   3. The method of initializing weights randomly
   4. The forward pass of input data through the network
   Show answer

   Answer: B. The process of propagating errors backward through the network to update weights

3. What is the primary purpose of feature engineering?

   1. To increase the number of model parameters
   2. To transform raw data into representations that improve model performance
   3. To reduce the number of training epochs required
   4. To replace the need for a validation set
   Show answer

   Answer: B. To transform raw data into representations that improve model performance

4. Which of the following is a common technique for handling missing data in a dataset?

   1. Increasing the learning rate
   2. Imputation using mean, median, or mode
   3. Adding more hidden layers
   4. Using a larger batch size
   Show answer

   Answer: B. Imputation using mean, median, or mode

5. What is the purpose of a confusion matrix in classification?

   1. To visualize the architecture of a neural network
   2. To plot the training loss over epochs
   3. To compute the gradient of the loss function
   4. To summarize true positives, true negatives, false positives, and false negatives
   Show answer

   Answer: D. To summarize true positives, true negatives, false positives, and false negatives

More questions in this quiz

Answers and explanations are in the full quiz.

1. What is the primary goal of unsupervised learning?
2. Which machine learning paradigm involves an agent learning by interacting with an environment to maximize cumulative reward?
3. What is the name of the process where a model learns patterns from a training dataset and then is evaluated on unseen data?
4. In machine learning, what does the term 'overfitting' refer to?
5. Which of the following is a common technique used to prevent overfitting in neural networks?
6. What is the main purpose of splitting a dataset into training, validation, and test sets?
7. Which term describes a model's ability to perform well on new, previously unseen data?
8. What is the role of a loss function in training a machine learning model?
9. Which of the following best describes 'deep learning'?
10. What is a key characteristic of a convolutional neural network (CNN)?
11. Which neural network architecture is specifically designed to handle sequential data and long-term dependencies?
12. What is the primary function of an activation function in a neural network?
13. Which of the following is a common activation function used in deep learning?
14. Which type of machine learning is used when you have a small amount of labeled data and a large amount of unlabeled data?
15. What is 'transfer learning' in the context of AI/ML?
16. Which AWS service can be used to build, train, and deploy machine learning models?
17. What is the purpose of the 'confusion matrix' in evaluating a classification model?
18. Which metric is most appropriate for evaluating a model when the dataset is highly imbalanced?
19. What is the primary difference between batch gradient descent and stochastic gradient descent (SGD)?
20. In the context of neural networks, what is 'vanishing gradient' problem?
21. Which technique is used to convert categorical variables into a numerical format suitable for machine learning algorithms?
22. What is the role of a hyperparameter in machine learning?
23. Which of the following is an example of a clustering algorithm?
24. Which of the following is the best definition of 'data leakage' in machine learning?
25. What does the 'bias-variance tradeoff' describe in machine learning?
26. Which of the following best describes 'reinforcement learning'?
27. Which of the following describes a 'generative' AI model?
28. What is the main difference between a parametric and a non-parametric model?
29. In NLP, what does 'tokenization' refer to?
30. Which of the following is a key difference between a transformer and an RNN?
31. What is the purpose of 'regularization' techniques such as L1 and L2?
32. What is 'embedding' in the context of NLP?
33. Which statement about 'epochs' vs 'iterations' is correct?
34. What is the primary role of the 'optimizer' in training a neural network?
35. What is 'F1 score' used for in model evaluation?
36. Which of the following is an example of an unsupervised learning task?
37. What does 'dimensionality reduction' aim to achieve?
38. Which of the following best describes 'model drift' in production ML systems?
39. What is the purpose of 'data augmentation' in training vision models?
40. In the context of ML, what does 'inference' mean?
41. Which of the following best describes 'AutoML'?
42. What is the primary difference between AI, ML, and deep learning?
43. Which of the following best describes 'semi-supervised learning'?
44. What is 'MLOps' primarily concerned with?
45. Which type of learning is used when a model is trained to predict a continuous numeric output, such as a house price?
46. What is the key characteristic of a decision tree model?
47. Which ensemble technique builds multiple models sequentially so that each new model corrects the errors of the previous ones?
48. What is the primary purpose of a validation set during model development?
49. Which of the following is a characteristic of the k-nearest neighbors (k-NN) algorithm?
50. Which evaluation metric is used for regression models to measure the average squared difference between predicted and actual values?
51. What does the term 'learning rate' control in gradient-based optimization?
52. Which of the following is a common regularization technique that randomly drops units during neural network training?
53. Which of the following best describes a 'hypothesis' in machine learning?
54. In the context of NLP, what does 'stemming' refer to?
55. Which of the following is a key difference between a generative model and a discriminative model?
56. What is the main purpose of a learning rate scheduler during training?
57. In reinforcement learning, what is the role of the 'discount factor'?
58. Which of the following is a method for handling class imbalance in a dataset?
59. What is the primary function of the softmax function in a neural network?
60. What is the purpose of the 'exploration' parameter in reinforcement learning?

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