19. Consider the following C code:
When the code is executed, what is the final value of x?
(A) 20
(B) 24
(C) 28
(D) 32
(E) 36

20. What will be the output of the following C code? (A) 36 (B) 39 (C) 93 (D) 63 (E) 96

21. In machine learning, one of the most common problems that will occur when we are training a model is overfitting. There are some techniques that could ease or even solve this problem. Which of the following technique does NOT alleviate overfitting problem? (A) Adding dropout between layers. (B) Increasing training epochs. (C) Data augmentation. (D) Early stopping while training model. (E) Using L1/L2 regularization.

22. Consider the following C program code and statements (1)~(5). (1) node[1].num = 0 (2) node[1].num = 1 (3) node[2].ptr-＞num = 9 (4) node[9].ptr-＞ptr-＞num = 121 (5) node[9].ptr-＞num = 100 Which of the following statements is CORRECT? (A) (1)(3)(4)(5) (B) (1)(3) (C) (2)(3) (D) (2)(3)(4)(5) (E) (4)(5)

23. The following is a C program code: Which one of the following statements is CORRECT? (A) When a=8, b=5, c=6, then A is 81, B is 5. (B) When a=8, b=5, c=6, then A is 81, B is 6. (C) When a=8, b=5, c=6, then A is 17, B is 5. (D) When a=8, b=5, c=6, then A is 17, B is 6. (E) When a=8, b=5, c=6, then A is 16, B is 6.

24. Suppose there are 16 users in the system, and they use symmetric encryption to achieve confidential communication, how many keys need to be managed? (A) 125 (B) 256 (C) 120 (D) 128 (E) 160

25. Let a, b, c be three 8-bit operands to a carry-save adder and their values be a = 00010110, b = 01101101 and c = 01001110. What are carry outputs of this carry-save adder? (A) 01001110 (B) 00110101 (C) 01010110 (D) 00111010 (E) 01101101

26. Which of the following activation functions can avoid the vanishing gradient problem? (A) ReLU (B) tanh (C) Sigmoid (D) ELU (E) None of the above

27. What is the status of a process if the process stops because its time slot is over? (A) Waiting (B) Running (C) Ready (D) Sleeping (E) Terminated

28. In machine learning, a confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. One can use the matrix to calculate the evaluation metrics, such as accuracy, precision, recall, and F1 score. Suppose a confusion matrix contains TP (true positive) = 900, FN (false negative) = 100, FP (false positive) = 50, and TN (true negative) = 950. What is the value of precision for the model? (A) 1850/2000 (B) 900/1000 (C) 900/2000 (D) 50/950 (E) 900/950

29. Suppose there is a two-input neuron with weights w1 = 3, w2 = 2, and bias b = 1.2. Let the activation function f be the ReLU (Rectified Linear Unit) function, defined as f(x) = max(0, x). Given the inputs p1 = -5 and p2 = 6. What is the output of this neuron? (A) 0 (B) 1.2 (C) 2.4 (D) -1.5 (E) -1.8

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