4. Cause of overflow Error (1D, binary math) - Tara Sehdave
In a certain computer program, two positive integers are added together, resulting in an overflow error. Which of the following best explains why the error occurs?
Responses A The program attempted to perform an operation that is considered an undecidable problem.
B The precision of the result is limited due to the constraints of using a floating-point representation.
C The program can only use a fixed number of bits to represent integers; the computed sum is greater than the maximum representable value.
D The program cannot represent integers; the integers are converted into decimal approximations, leading to rounding errors.
The correct answer is C because…
- Overflow error occurs in a computer program when adding two positive integers.
- The program uses a fixed number of bits to represent integers.
- The sum of the two integers exceeds the maximum representable value within the fixed number of bits.
- Due to this limitation, the program cannot accurately represent the result of the addition.
- As a result, an overflow error is triggered to indicate that the computed sum is beyond the representable range.
# python overflow example
# as a popcorn hack (binary challenge), describe an overflow in 8 binary digits
# Add 1 to 11111111 (255)
# Subtract 1 from 00000000 (0)
# Try overflow and underflow here: https://nighthawkcoders.github.io/teacher_portfolio/c4.4/2023/09/14/javascript-binary-U2-1.html44
import sys
# Maximum float
max_float = sys.float_info.max
print(f"Max float: {max_float}")
# Attempt to overflow float
overflow_float = max_float * 2
print(f"Overflow float to infinity: {overflow_float}")
Describe an overflow in 8 binary digits:
In binary, an overflow occurs when the result of an arithmetic operation exceeds the maximum representable value with the given number of bits. For example, in an 8-bit system, the maximum representable value is 255 (binary: 11111111). If you try to add 1 to this value, it will overflow because there are only 8 bits available to represent the result, and the result will be 256 (binary: 100000000), which cannot be represented with only 8 bits.
Add 1 to 11111111 (255):
When you add 1 to 11111111 (255) in binary, the result overflows the 8-bit representation, and you get 100000000, which is equivalent to 256 in decimal. This represents an overflow.
Subtract 1 from 00000000 (0):
When you subtract 1 from 00000000 (0) in binary, the result would be 11111111, which is equivalent to 255 in decimal. This represents an underflow, where the result goes below the minimum representable value (0 in this case) and wraps around to the maximum representable value.
5. Inputs to Logic Circuit (2D, binary logic) - Hanlun Li
The diagram below shows a circuit composed of three logic gates. Each gate takes two inputs and produces a single output. For which of the following input values will the circuit have an output of true ?
The diagram in question shows a circuit composed of three logic gates. Each gate takes two inputs and produces a single output.
For which of the following input values will the circuit have an output of true ?
A) A = true, B = true, C = true, D = false
B) A = true, B = false, C = false, D = true
C) A = false, B = true, C = true, D = true
D) A = false, B = false, C = true, D = true
A OR B = True, C AND D = True, and since True AND True = True, C is correct.
OR gate only returns true if one of the values is true, and the AND gate returns true if both values are true.
# represent gates and circuits
# as a popcorn hack (coding challenge), create multiple new circuits and gates
# NOT, NAND, NOR, XOR, XNOR
# Create a hypothetical circuit, such as burglar alarm, decision tree for autonomous car, etc.
# OR gate
def OR_gate(A, B):
return A or B
# AND gate
def AND_gate(A, B):
return A and B
# Theoritical circuit representing a Car starting
# A and B could be security checks, such as key being inserted or a fob being present
# C and D could be operational checks, such as a start button being pressed and safety belt being fastened
# The enclosing AND gate ensures car only operates when both security and operational checks are met
def circuit(A, B, C, D):
return AND_gate(OR_gate(A, B), AND_gate(C, D))
# Print truth table for circuit
print('A', 'B', 'C', 'D', "-->", 'Output')
# nesting of loops for both the True, False combination of A, B, C, D
# this algorithm is 2 ^ 4 = 16, thus producing all 16 combinations
# each combination terminates with the output of the circuit
for A in [False, True]:
for B in [False, True]:
for C in [False, True]:
for D in [False, True]:
print(A, B, C, D, "-->", circuit(A, B, C, D))
# Define logic gates
def NOT(input):
return 1 - input
def NAND(input1, input2):
return NOT(input1 * input2)
def NOR(input1, input2):
return NOT(input1 + input2)
def XOR(input1, input2):
return (input1 + input2) % 2
def XNOR(input1, input2):
return NOT((input1 + input2) % 2)
# Hypothetical Decision Tree for an Autonomous Car
def autonomous_car_decision_tree(speed, obstacle_distance, traffic_light):
# First layer of logic gates
slow_down = NOR(speed, obstacle_distance)
stop = NAND(obstacle_distance, traffic_light)
# Second layer of logic gates
decision = XOR(slow_down, stop)
return decision
# Test the decision tree for the autonomous car
speed = 0 # Assume the car is driving at a slow speed
obstacle_distance = 1 # Assume there is an obstacle at a short distance
traffic_light = 0 # Assume the traffic light is green
decision = autonomous_car_decision_tree(speed, obstacle_distance, traffic_light)
if decision == 1:
print("Autonomous car should take action.")
else:
print("Autonomous car can continue driving safely.")
Explaining the code for the popcorn hack above:
NOT Gate (NOT): Inverts the input (0 becomes 1, and 1 becomes 0).
NAND Gate (NAND): Produces 1 unless both inputs are 1.
NOR Gate (NOR): Produces 1 unless either input is 1.
XOR Gate (XOR): Produces 1 if inputs are different.
XNOR Gate (XNOR): Produces 1 if inputs are the same.
Autonomous Car Decision Tree: The function autonomous_car_decision_tree takes inputs for speed, obstacle distance, and traffic light. It determines whether the car should slow down or stop based on the inputs using NOR and NAND gates. The final decision is made using an XOR gate, determining if the car should take action. Testing: Hypothetical inputs are used to test the decision tree, determining if the car should take action based on the scenario.
11. Color represented by binary Triplet (2D, binary) - Torin Wolff
A color in a computing application is represented by an RGB triplet that describes the amount of red, green, and blue, respectively, used to create the desired color. A selection of colors and their corresponding RGB triplets are shown in the following table. Each value is represented in decimal (base 10).
According to information in the table, what color is represented by the binary RGB triplet (11111111, 11111111, 11110000) ?
What is a binary triplet?
A binary triplet is a set of three binary numbers. Binary numbers are numbers that are represented by a series of 1’s and 0’s. Binary numbers are used in computers because they are easy to represent with electrical signals. A binary triplet is a set of three binary numbers that are used to represent a color. The first number represents the amount of red in the color, the second number represents the amount of green in the color, and the third number represents the amount of blue in the color. The numbers are represented in binary because it is easy to represent with electrical signals. The numbers are represented in a triplet because it is easy to represent with electrical signals.
How is a color in binary represented?
A color in binary is represented by a set of three binary numbers. The first number represents the amount of red in the color, the second number represents the amount of green in the color, and the third number represents the amount of blue in the color. The numbers are represented in binary because it is easy to represent with electrical signals. The numbers are represented in a triplet because it is easy to represent with electrical signals. An example of this would be: (01101110, 11111111, 10010110), if you converted this binary triplet to a decimal number it would be 110, 255, 150. This would be a shade of the color green.
How do you convert a binary triplet to a decimal number?
To convert a binary string to a decimal number you need to multiply each digit by its place value. For example, if you have the binary string 1010 you would multiply the first digit by 2^3, the second digit by 2^2, the third digit by 2^1, and the fourth digit by 2^0. This would give you the decimal number 10. To convert a binary triplet to a decimal number you need to multiply each digit by its place value. For example, if you have the binary triplet (01101110, 11111111, 10010110) you would multiply the first digit by 2^7, the second digit by 2^6, the third digit by 2^5, the fourth digit by 2^4, the fifth digit by 2^3, the sixth digit by 2^2, the seventh digit by 2^1, and the eighth digit by 2^0. This would give you the decimal number 110, 255, 150.
(0 * 2^7) + (1 * 2^6) + (1 * 2^5) + (0 * 2^4) + (1 * 2^3) + (1 * 2^2) + (1 * 2^1) + (0 * 2^0)
= 0 + 64 + 32 + 0 + 8 + 4 + 2 + 0
= 110
(insert photo)
# Convert binary RGB triplet to decimal
# as a hack (binary challenge), make the rgb standard colors
# as a 2nd hack, make your favorite color pattern
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Function to convert binary to decimal
def binary_to_decimal(binary):
return int(binary, 2)
def plot_colors(rgb_triplets):
# Create a figure with one subplot per RGB triplet
fig, axs = plt.subplots(1, len(rgb_triplets), figsize=(2 * len(rgb_triplets), 2))
# Ensure axs is always a list
axs = axs if len(rgb_triplets) > 1 else [axs]
for ax, (red_binary, green_binary, blue_binary) in zip(axs, rgb_triplets):
# Convert to binary strings to decimal
red_decimal = binary_to_decimal(red_binary)
green_decimal = binary_to_decimal(green_binary)
blue_decimal = binary_to_decimal(blue_binary)
# Normalize number to [0, 1] range, as it is expected by matplotlib
red, green, blue = red_decimal/255, green_decimal/255, blue_decimal/255
# Define a rectangle patch with the binary RGB triplet color and a black border
rect = patches.Rectangle((0, 0), 1, 1, facecolor=(red, green, blue), edgecolor='black', linewidth=2)
# Add the rectangle to the plot which shows the color
ax.add_patch(rect)
# Remove axis information, we just want to see the color
ax.axis('off')
# Print the binary and decimal values
print("binary:", red_binary, green_binary, blue_binary)
print("decimal", red_decimal, green_decimal, blue_decimal)
print("proportion", red, green, blue)
# Show the colors
plt.show()
# Test the function with a list of RGB triplets
rgb_triplet = [('11111111', '11111111', '11110000')] # College Board example
plot_colors(rgb_triplet)
rgb_primary = [('11111111', '00000000', '00000000'),
('11111111', '11111111', '00000000'),
('00000000', '00000000', '11111111')]
plot_colors(rgb_primary)
# Function to convert binary RGB triplet to decimal
def binary_to_decimal(binary):
decimal = int(binary, 2)
return decimal
# Function to convert decimal RGB triplet to binary
def decimal_to_binary(decimal):
binary = bin(decimal)[2:].zfill(8) # Convert decimal to binary with leading zeros
return binary
# Standard RGB colors in binary
standard_colors = {
"Red": "11111111",
"Green": "00000111",
"Blue": "00000000",
"Yellow": "11111111", # Red + Green
"Magenta": "11110000", # Red + Blue
"Cyan": "00001111", # Green + Blue
"White": "11111111" # Red + Green + Blue
}
# Convert standard RGB colors to decimal
standard_colors_decimal = {color: binary_to_decimal(value) for color, value in standard_colors.items()}
# Print standard RGB colors in decimal
print("Standard RGB Colors (Decimal):")
for color, decimal_value in standard_colors_decimal.items():
print(f"{color}: {decimal_value}")
# Favorite color pattern in binary
favorite_colors = {
"MyFavoriteColor1": "11001100",
"MyFavoriteColor2": "10101010",
"MyFavoriteColor3": "11100000"
}
# Convert favorite color pattern to decimal
favorite_colors_decimal = {color: binary_to_decimal(value) for color, value in favorite_colors.items()}
# Print favorite color pattern in decimal
print("\nFavorite Color Pattern (Decimal):")
for color, decimal_value in favorite_colors_decimal.items():
print(f"{color}: {decimal_value}")
Explaining code to popcorn hack above
binary_to_decimal function converts a binary RGB triplet to a decimal value. decimal_to_binary function converts a decimal RGB triplet to a binary value. We define standard RGB colors and their binary representations. We convert standard RGB colors to decimal using the binary_to_decimal function. We define a favorite color pattern and its binary representations. We convert the favorite color pattern to decimal using the binary_to_decimal function.
50. Reasonable time algorithms (1D, Big O) - Vance Reynolds
Consider the following algorithms. Each algorithm operates on a list containing n elements, where n is a very large integer.
- An algorithm that accesses each element in the list twice
- An algorithm that accesses each element in the list n times
- An algorithm that accesses only the first 10 elements in the list, regardless of the size of the list
Which of the algorithms run in reasonable time? Answer D is correct because in order for an algorithm to run in reasonable time, it must take a number of steps less than or equal to a polynomial function.
- Algorithm I accesses elements times (twice for each of n elements), which is considered in time.
- Algorithm II accesses elements (n times for each of n elements), which is in reasonable time.
- Algorithm III accesses 10 elements, which is in reasonable time.
Simple Explainations:
Unreasonable time: Algorithms with exponential or factorial efficiencies are examples of algorithms that run in an unreasonable amount of time.
Reasonable time: Algorithms with a polynomial efficiency or lower (constant, linear, square, cube, etc.) are said to run in a reasonable amount of time.
# Big O notation example algorithms
# as a popcorn hack (coding challenge), scale list of size by factor of 10 and measure the times
# what do you think about college board's notion of reasonable time for an algorithm?
# as a 2nd hack, create a slow algorithm and measure its time, which are considered slow algorithms...
# O(n^3) which is three nested loops
# O(2^n) which is a recursive algorithm with two recursive calls
import time
# O(n) Algorithm that accesses each element in the list twice, 2 * n times
def algorithm_2n(lst):
for i in lst:
pass
for i in lst:
pass
# O(n^2) Algorithm that accesses each element in the list n times, n * n times
def algorithm_nSquared(lst):
for i in lst:
for j in lst:
pass
# O(1) Algorithm that accesses only the first 10 elements in the list, 10 * 1 is constant
def algorithm_10times(lst):
for i in lst[:10]:
pass
# Create a large list
n = 10000
lst = list(range(n))
# Measure the time taken by algorithm1
start = time.time()
algorithm_2n(lst)
end = time.time()
print(f"Algorithm 2 * N took {(end - start)*1000:.2f} milliseconds")
# Measure the time taken by algorithm2
start = time.time()
algorithm_nSquared(lst)
end = time.time()
print(f"Algorithm N^2 took {(end - start)*1000:.2f} milliseconds")
# Measure the time taken by algorithm3
start = time.time()
algorithm_10times(lst)
end = time.time()
print(f"Algorithm 10 times took {(end - start)*1000:.2f} milliseconds")
import time
# Function to perform some operation on a list and measure time
def measure_time(operation, input_list):
start_time = time.time()
operation(input_list)
end_time = time.time()
execution_time = end_time - start_time
return execution_time
# Example of a slow algorithm with O(n^3) time complexity
def slow_algorithm_n3(input_list):
n = len(input_list)
for i in range(n):
for j in range(n):
for k in range(n):
pass # Some operation here
# Example of a slow algorithm with O(2^n) time complexity
def slow_algorithm_2n(n):
if n <= 1:
return n
else:
return slow_algorithm_2n(n-1) + slow_algorithm_2n(n-2)
# Test scaling list and measuring time
for scale_factor in range(1, 6): # Scale list size by factor of 10
input_list = [0] * (10**scale_factor)
execution_time = measure_time(slow_algorithm_n3, input_list)
print(f"List size: {len(input_list)}, Execution Time: {execution_time} seconds")
# Test slow algorithms and measure time
n = 20 # Input size for O(2^n) algorithm
execution_time_n3 = measure_time(slow_algorithm_n3, input_list)
execution_time_2n = measure_time(slow_algorithm_2n, n)
print(f"Execution Time for O(n^3) algorithm: {execution_time_n3} seconds")
print(f"Execution Time for O(2^n) algorithm: {execution_time_2n} seconds")
Explaining code for popcorn hack above
Measuring Time: The measure_time function measures the execution time of an operation on an input list.
Slow Algorithms: slow_algorithm_n3 demonstrates O(n^3) time complexity with three nested loops. slow_algorithm_2n demonstrates O(2^n) time complexity with naive recursion.
Testing: The code tests the scalability of slow_algorithm_n3 by increasing the list size by a factor of 10. It also measures the execution time of both slow_algorithm_n3 and slow_algorithm_2n.
Output: The execution times of the algorithms are printed for observation.
56. Compare execution times of tow version (1D analysis) - Kayden Le
An online game collects data about each player’s performance in the game. A program is used to analyze the data to make predictions about how players will perform in a new version of the game.
The procedure GetPrediction (idNum) returns a predicted score for the player with ID number idNum. Assume that all predicted scores are positive. The GetPrediction procedure takes approximately 1 minute to return a result. All other operations happen nearly instantaneously.
Two versions of the program are shown below.
Which of the following best compares the execution times of the two versions of the program?
Version I calls the GetPrediction procedure once for each element of idList, or four times total. Since each call requires 1 minute of execution time, version I requires approximately 4 minutes to execute. Version II calls the GetPrediction procedure twice for each element of idList, and then again in the final display statement. This results in the procedure being called nine times, requiring approximately 9 minutes of execution time.
Both versions aim to achieve the same result, which is to find and display the highest predicted score among the players in idList. However, Version I directly updates the highest score (topScore), while Version II updates the ID of the player with the highest score (topID) and then retrieves the predicted score for that player. Version II essentially avoids calling GetPrediction multiple times for the same ID.
The answer: D - Version II requires approximately 5 more minutes to execute than version I
# Simulate the time taken by GetPrediction, calling an expensive operation twice is slow
# as a popcorn hack (coding challenge)
# measure the time to calulate fibonacci sequence at small and large numbers
# `this task will require a code layout and a time measurement
# there are fast ways and slow ways to calculate fibonacci sequence`
'''
The GetPrediction function is a simulation stand-in for a potentially expensive operation.
In a real-world scenario, this could be a call to a;
- machine learning model
- a database query
- fibonacci sequence at a large number
- or any other algorithm that takes a significant amount of time.
'''
def GetPrediction(idNum):
# time.sleep(60) # Commented out to avoid actual delay
return idNum * 10 # Dummy prediction
# Version I: makes a single call to GetPrediction for each element in idList
def version_I(idList):
start = time.time() # Start timer
topScore = 0
for idNum in idList:
predictedScore = GetPrediction(idNum) # calls GetPrediction once
if predictedScore > topScore:
topScore = predictedScore # stores the topScore to avoid calling GetPrediction again
end = time.time() # End timer
print(f"Version I took {end - start + len(idList) * 60:.2f} simulated seconds") # Add simulated delay
# Version II: makes two calls to GetPrediction for each element in idList
def version_II(idList):
start = time.time() # Start timer
topID = idList[0]
for idNum in idList:
if GetPrediction(idNum) > GetPrediction(topID): # calls GetPrediction twice
topID = idNum # stores the topID, but still calls GetPrediction with topID again
end = time.time() # End timer
print(f"Version II took {end - start + len(idList) * 2 * 60 + 60:.2f} simulated seconds") # Add simulated delay
# Test the functions
idList = [1, 2, 3, 4] # Small list
# idList = [i for i in range(1, 1000)] # Large list using list comprehension
version_I(idList)
version_II(idList)
import time
# Function to calculate Fibonacci sequence recursively (slow way)
def fibonacci_recursive(n):
if n <= 1:
return n
else:
return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)
# Function to calculate Fibonacci sequence iteratively (fast way)
def fibonacci_iterative(n):
if n <= 1:
return n
else:
fib = [0, 1]
for i in range(2, n+1):
fib.append(fib[i-1] + fib[i-2])
return fib[n]
# Function to measure the time taken for Fibonacci calculation
def measure_time(fib_function, n):
start_time = time.time()
fib_result = fib_function(n)
end_time = time.time()
execution_time = end_time - start_time
return fib_result, execution_time
# Test Fibonacci calculation for small and large numbers
small_number = 10 # Small number for testing
large_number = 35 # Large number for testing
# Test and measure time for small number
fib_result_small, execution_time_small = measure_time(fibonacci_recursive, small_number)
print(f"Fibonacci({small_number}): {fib_result_small}, Execution Time (Recursive): {execution_time_small} seconds")
fib_result_small, execution_time_small = measure_time(fibonacci_iterative, small_number)
print(f"Fibonacci({small_number}): {fib_result_small}, Execution Time (Iterative): {execution_time_small} seconds")
# Test and measure time for large number
fib_result_large, execution_time_large = measure_time(fibonacci_recursive, large_number)
print(f"Fibonacci({large_number}): {fib_result_large}, Execution Time (Recursive): {execution_time_large} seconds")
fib_result_large, execution_time_large = measure_time(fibonacci_iterative, large_number)
print(f"Fibonacci({large_number}): {fib_result_large}, Execution Time (Iterative): {execution_time_large} seconds")
Explaining code to popcorn hack above
fibonacci_recursive calculates Fibonacci sequence recursively, which is relatively slow. fibonacci_iterative calculates Fibonacci sequence iteratively, which is faster than the recursive approach. measure_time measures the execution time of a Fibonacci calculation function. We test Fibonacci calculation for a small number (small_number) and a large number (large_number) using both recursive and iterative approaches. We print the Fibonacci result and the execution time for both small and large numbers with both recursive and iterative approaches.
64. Error with multiplication using repeated addition (4C algorithms and programs) - Abdullah Khanani
The following procedure is intended to return the value of x times y, where x and y are integers. Multiplication is implemented using repeated additions.
For which of the following procedure calls does the procedure NOT return the intended value?
Select two answers.
Question 64
PROCEDURE Multiply [x,y] count <– 0 result <– 0 REPEAT UNTIL [count ≥ y] result <– result + x count <– count + 1 RETURN result
Question
For which of the following procedure calls does the procedure NOT return the intended value? Select two answers.
Options
A. Multiply 2,5 B. Multiply 2,-5 C. Multiply -2,5 D. Multiply -2,-5
Answered
A and C
Correct Answer
B and D
Explanation
For procedures A, the procedure repeatedly adds 2 to result five times, resulting in the intended product 10. Vice versa for C. IF you want to return the result and get a correct answer, multiplying 2,-5 and -2,-5 will give you the wanted result. The following procedure is intended to return the value of x times y, where x and y are integers. Multiplication is implemented using repeated additions. A and C fit these requirements.
# flawed multiply function
# as a popcorn hack (coding challenge), fix the multiply function to work with negative numbers
'''
As you can see, the function fails when y is negative,
because the while loop condition count < y is never true in these cases.
'''
def multiply(x, y):
count = 0
result = 0
while count < y:
result += x
count += 1
return result
# Test cases
print(multiply(2, 5)) # Expected output: 10
print(multiply(2, -5)) # Expected output: -10, Actual output: 0
print(multiply(-2, 5)) # Expected output: -10
print(multiply(-2, -5)) # Expected output: 10, Actual output: 0
def multiply(x, y):
if y == 0: # If y is 0, result is 0
return 0
result = 0
if y > 0: # If y is positive, use y as the count
count = y
while count > 0:
result += x
count -= 1
else: # If y is negative, use -y as the count and negate the result
count = -y
while count > 0:
result -= x
count -= 1
return result
# Test cases
print(multiply(2, 5)) # Expected output: 10
print(multiply(2, -5)) # Expected output: -10
print(multiply(-2, 5)) # Expected output: -10
print(multiply(-2, -5)) # Expected output: 10
Explaining code to popcorn hack above
We first handle the case when y is 0 by returning 0. For positive y, we use y as the count in the loop. For negative y, we use -y as the count and negate the result in each iteration of the loop. Now, the function should correctly handle negative values of y and provide the expected output for all test cases.
65. Call to concat and substring (4B string operations) - Ameer Hussain
A program contains the following procedures for string manipulation.
Which of the following can be used to store the string “jackalope” in the string variable animal ?
Select two answers.
Correct Answer C:
animal ← Substring(“jackrabbit”, 1, 4) extracts “jack” from “jackrabbit”. animal ← Concat(animal, “a”) appends “a” to “jack”, resulting in “jacka”. animal ← Concat(animal, Substring(“antelope”, 5, 4)) extracts “lope” from “antelope” and appends it to “jacka”, resulting in “jackalope”.
Incorrect Answer D:
animal ← Substring(“jackrabbit”, 1, 4) extracts “jack” from “jackrabbit”. animal ← Concat(animal, “a”) appends “a” to “jack”, resulting in “jacka”. animal ← Concat(Substring(“antelope”, 5, 4), animal) is slightly misleading because it extracts “lope” from “antelope” and should prepend it to “jacka”, but this is incorrect because it would result in “lopejacka”. the operations in Answer D would not result in “jackalope”.
animal ← Substring(“antelope”, 5, 4) would assign the substring “lope” from “antelope” to the variable animal. animal ← Concat(“a”, animal) would then prepend “a” to “lope”, resulting in “alope”. animal ← Concat(Substring(“jackrabbit”, 1, 4), animal) would take the substring “jack” from “jackrabbit” and then concatenate it with “alope” resulting in “jackalope”.
The process outlined in Answer B does indeed result in the string “jackalope”, with the first step isolating “lope” from “antelope”, the second step creating the string “alope” by prepending “a” to “lope”, and the final step creating the correct string by prepending “jack” to “alope”.
animal ← Substring(“antelope”, 5, 4) would assign the substring “lope” from “antelope” to the variable animal. animal ← Concat(“a”, animal) would then prepend “a” to “lope”, resulting in “alope”. animal ← Concat(Substring(“jackrabbit”, 1, 4), animal) would take the substring “jack” from “jackrabbit” and then concatenate it with “alope” resulting in “jackalope”.
The process outlined in Answer B does indeed result in the string “jackalope”, with the first step isolating “lope” from “antelope”, the second step creating the string “alope” by prepending “a” to “lope”, and the final step creating the correct string by prepending “jack” to “alope”.
# Incorrect Answer D
# as a popcorn hack (binary challenge), create string and concatenation options for A, B, C
animal = "jackrabbit"[0:4] # Substring("jackrabbit", 1, 4)
animal += "a" # Concat(animal, "a")
animal = "antelope"[4:8] + animal # Concat(Substring("antelope", 5, 4), animal)
print(animal) # Outputs: lopejacka
# Define the initial animal string by taking a substring of "jackrabbit"
animal = "jackrabbit"[0:4] # animal = "jack"
# Append the letter 'a' to the animal string
animal += "a" # animal = "jacka"
# Modify the animal string by concatenating a substring of "antelope" and the existing animal string
animal = "antelope"[4:8] + animal # animal = "lope" + "jacka" = "lopejacka"
# Print the final value of the animal string
print(animal) # Outputs: lopejacka
Explaining code to popcorn hack above
The animal variable is initially set to “jack” by taking a substring of “jackrabbit”. The letter ‘a’ is appended to the animal string, resulting in “jacka”. Then, a substring of “antelope” from index 4 to 8 (“lope”) is concatenated with the existing animal string, resulting in “lopejacka”. Finally, the value of the animal variable is printed, which outputs “lopejacka”.