- An abstract class is the name for any class from which you can instantiate an object.
- Abstract classes must be redefined any time an object is instantiated from them.
- Abstract classes must inherit from concrete classes.
- An abstract class exists only so that other "concrete" classes can inherit from the abstract class.
- The
any()
function will randomly return any item from the list. - The
any()
function returns True if any item in the list evaluates to True. Otherwise, it returns False. - The
any()
function takes as arguments the list to check inside, and the item to check for. If "any" of the items in the list match the item to check for, the function returns True. - The
any()
function returns a Boolean value that answers the question "Are there any items in this list?"
- linked list
- queue
- set
- OrderedDict
- Static methods are called static because they always return
None
. - Static methods can be bound to either a class or an instance of a class.
- Static methods serve mostly as utility methods or helper methods, since they can't access or modify a class's state.
- Static methods can access and modify the state of a class or an instance of a class.
- Attributes are long-form version of an
if/else
statement, used when testing for equality between objects. - Attributes are a way to hold data or describe a state for a class or an instance of a class.
- Attributes are strings that describe characteristics of a class.
- Function arguments are called "attributes" in the context of class methods and instance methods.
count, fruit, price = (2, 'apple', 3.5)
-
tuple assignment
-
tuple unpacking
-
tuple matching
-
tuple duplication
-
.delete()
method -
pop(my_list)
-
del(my_list)
-
.pop()
method
- to capture command-line arguments given at a file's runtime
- to connect various systems, such as connecting a web front end, an API service, a database, and a mobile app
- to take a snapshot of all the packages and libraries in your virtual environment
- to scan the health of your Python ecosystem while inside a virtual environment
- O(n), also called linear time.
- O(log n), also called logarithmic time.
- O(n^2), also called quadratic time.
- O(1), also called constant time.
-
class Game: pass
-
def Game(): pass
-
def Game: pass
-
class Game(): pass
- A
def sum(a, b):
"""
sum(4, 3)
7
sum(-4, 5)
1
"""
return a + b
- B
def sum(a, b):
"""
>>> sum(4, 3)
7
>>> sum(-4, 5)
1
"""
return a + b
- C
def sum(a, b):
"""
# >>> sum(4, 3)
# 7
# >>> sum(-4, 5)
# 1
"""
return a + b
- D
def sum(a, b):
###
>>> sum(4, 3)
7
>>> sum(-4, 5)
1
###
return a + b
-
set
-
list
-
None
-
dictionary
. You can only build a stack from scratch.
college_years = ['Freshman', 'Sophomore', 'Junior', 'Senior']
return list(enumerate(college_years, 2019))
-
[('Freshman', 2019), ('Sophomore', 2020), ('Junior', 2021), ('Senior', 2022)]
-
[(2019, 2020, 2021, 2022), ('Freshman', 'Sophomore', 'Junior', 'Senior')]
-
[('Freshman', 'Sophomore', 'Junior', 'Senior'), (2019, 2020, 2021, 2022)]
-
[(2019, 'Freshman'), (2020, 'Sophomore'), (2021, 'Junior'), (2022, 'Senior')]
-
defaultdict
will automatically create a dictionary for you that has keys which are the integers 0-10. -
defaultdict
forces a dictionary to only accept keys that are of the types specified when you created thedefaultdict
(such as string or integers). - If you try to access a key in a dictionary that doesn't exist,
defaultdict
will create a new key for you instead of throwing aKeyError
. -
defaultdict
stores a copy of a dictionary in memory that you can default to if the original gets unintentionally modified.
Q15. What is the correct syntax for defining a class called "Game", if it inherits from a parent class called "LogicGame"?
-
class Game.LogicGame(): pass
-
def Game(LogicGame): pass
-
class Game(LogicGame): pass
-
def Game.LogicGame(): pass
-
self
means that no other arguments are required to be passed into the method. - There is no real purpose for the
self
method; it's just historic computer science jargon that Python keeps to stay consistent with other programming languages. -
self
refers to the instance whose method was called. -
self
refers to the class that was inherited from to create the object usingself
.
- You can assign a name to each of the
namedtuple
members and refer to them that way, similarly to how you would access keys indictionary
. - Each member of a namedtuple object can be indexed to directly, just like in a regular
tuple
. -
namedtuples
are just as memory efficient as regulartuples
. - No import is needed to use
namedtuples
because they are available in the standard library.
- Instance methods can modify the state of an instance or the state of its parent class.
- Instance methods hold data related to the instance.
- An instance method is any class method that doesn't take any arguments.
- An instance method is a regular function that belongs to a class, but it must return
None
.
- [ ]
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
elif num_people > 4:
print("There are some people in the pool.")
elif num_people > 0:
print("There are a few people in the pool.")
else:
print("There is no one in the pool.")
- [ ]
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
if num_people > 4:
print("There are some people in the pool.")
if num_people > 0:
print("There are a few people in the pool.")
else:
print("There is no one in the pool.")
- [x]
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
elif num_people > 4:
print("There are some people in the pool.")
elif num_people > 0:
print("There are a few people in the pool.")
else:
print("There is no one in the pool.")
- [ ]
if num_people > 10;
print("There is a lot of people in the pool.")
if num_people > 4:
print("There are some people in the pool.")
if num_people > 0:
print("There are a few people in the pool.")
else:
print("There is no one in the pool.")
Also see Question 85 for the same question with different answers.
- It protects the data from outside interference.
- A parent class is encapsulated and no data from the parent class passes on to the child class.
- It keeps data and the methods that can manipulate that data in one place.
- It only allows the data to be changed by methods.
- It tells the computer which chunk of code to run if the instructions you coded are incorrect.
- It runs one chunk of code if all the imports were successful, and another chunk of code if the imports were not successful.
- It executes one chunk of code if a condition is true, but a different chunk of code if the condition is false.
- It tells the computer which chunk of code to run if the is enough memory to handle it, and which chunk of code to run if there is not enough memory to handle it.
-
dictionary
-
set
-
None
-
list
You can only build a stack from scratch.
-
my_game = class.Game()
-
my_game = class(Game)
-
my_game = Game()
-
my_game = Game.create()
- It creates a path from multiple values in an iterable to a single value.
- It applies a function to each item in an iterable and returns the value of that function.
- It converts a complex value type into simpler value types.
- It creates a mapping between two different elements of different iterables.
- The function will return a RuntimeError if you don't return a value.
- If the return keyword is absent, the function will return
None
. - If the return keyword is absent, the function will return
True
. - The function will enter an infinite loop because it won't know when to stop executing its code.
- It is used to skip the
yield
statement of a generator and return a value of None. - It is a null operation used mainly as a placeholder in functions, classes, etc.
- It is used to pass control from one statement block to another.
- It is used to skip the rest of a
while
orfor loop
and return to the start of the loop.
- arguments
- paradigms
- attributes
- decorators
-
slot
-
dictionary
-
queue
-
sorted list
- when it encounters an infinite loop
- when it encounters an if/else statement that contains a break keyword
- when it has assessed each item in the iterable it is working on or a break keyword is encountered
- when the runtime for the loop exceeds O(n^2)
Q30. Assuming the node is in a singly linked list, what is the runtime complexity of searching for a specific node within a singly linked list?
- The runtime is O(n) because in the worst case, the node you are searching for is the last node, and every node in the linked list must be visited.
- The runtime is O(nk), with n representing the number of nodes and k representing the amount of time it takes to access each node in memory.
- The runtime cannot be determined unless you know how many nodes are in the singly linked list.
- The runtime is O(1) because you can index directly to a node in a singly linked list.
Q31. Given the following three list, how would you create a new list that matches the desired output printed below?
fruits = ['Apples', 'Oranges', 'Bananas']
quantities = [5, 3, 4]
prices = [1.50, 2.25, 0.89]
#Desired output
[('Apples', 5, 1.50),
('Oranges', 3, 2.25),
('Bananas', 4, 0.89)]
- [ ]
output = []
fruit_tuple_0 = (first[0], quantities[0], price[0])
output.append(fruit_tuple)
fruit_tuple_1 = (first[1], quantities[1], price[1])
output.append(fruit_tuple)
fruit_tuple_2 = (first[2], quantities[2], price[2])
output.append(fruit_tuple)
return output
- [x]
i = 0
output = []
for fruit in fruits:
temp_qty = quantities[i]
temp_price = prices[i]
output.append((fruit, temp_qty, temp_price))
i += 1
return output
- [ ]
groceries = zip(fruits, quantities, prices)
return groceries
>>> [
('Apples', 5, 1.50),
('Oranges', 3, 2.25),
('Bananas', 4, 0.89)
]
- [ ]
i = 0
output = []
for fruit in fruits:
for qty in quantities:
for price in prices:
output.append((fruit, qty, price))
i += 1
return output
- The
all()
function returns a Boolean value that answers the question "Are all the items in this list the same? - The
all()
function returns True if all the items in the list can be converted to strings. Otherwise, it returns False. - The
all()
function will return all the values in the list. - The
all()
function returns True if all items in the list evaluate to True. Otherwise, it returns False.
(Answer format may vary. Game and roll (or dice_roll) should each be called with no parameters.)
- [x]
>>> dice = Game()
>>> dice.roll()
- [ ]
>>> dice = Game(self)
>>> dice.roll(self)
- [ ]
>>> dice = Game()
>>> dice.roll(self)
- [ ]
>>> dice = Game(self)
>>> dice.roll()
- backtracking
- dynamic programming
- decrease and conquer
- divide and conquer
- O(1), also called constant time
- O(log n), also called logarithmic time
- O(n^2), also called quadratic time
- O(n), also called linear time
- A set is an ordered collection unique items. A list is an unordered collection of non-unique items.
- Elements can be retrieved from a list but they cannot be retrieved from a set.
- A set is an ordered collection of non-unique items. A list is an unordered collection of unique items.
- A set is an unordered collection unique items. A list is an ordered collection of non-unique items.
- Abstraction means that a different style of code can be used, since many details are already known to the program behind the scenes.
- Abstraction means the implementation is hidden from the user, and only the relevant data or information is shown.
- Abstraction means that the data and the functionality of a class are combined into one entity.
- Abstraction means that a class can inherit from more than one parent class.
def print_alpha_nums(abc_list, num_list):
for char in abc_list:
for num in num_list:
print(char, num)
return
print_alpha_nums(['a', 'b', 'c'], [1, 2, 3])
- [x]
a 1
a 2
a 3
b 1
b 2
b 3
c 1
c 2
c 3
- [ ]
['a', 'b', 'c'], [1, 2, 3]
- [ ]
aaa
bbb
ccc
111
222
333
- [ ]
a 1 2 3
b 1 2 3
c 1 2 3
- [x]
my_game = Game()
my_game.roll_dice()
- [ ]
my_game = Game()
self.my_game.roll_dice()
- [ ]
my_game = Game(self)
self.my_game.roll_dice()
- [ ]
my_game = Game(self)
my_game.roll_dice(self)
- [ ]
def sum(a, b):
# a = 1
# b = 2
# sum(a, b) = 3
return a + b
- [ ]
def sum(a, b):
"""
a = 1
b = 2
sum(a, b) = 3
"""
return a + b
- [x]
def sum(a, b):
"""
>>> a = 1
>>> b = 2
>>> sum(a, b)
3
"""
return a + b
- [ ]
def sum(a, b):
'''
a = 1
b = 2
sum(a, b) = 3
'''
return a + b
Q41. Suppose a Game class inherits from two parent classes: BoardGame and LogicGame. Which statement is true about the methods of an object instantiated from the Game class?
- When instantiating an object, the object doesn't inherit any of the parent class's methods.
- When instantiating an object, the object will inherit the methods of whichever parent class has more methods.
- When instantiating an object, the programmer must specify which parent class to inherit methods from.
- An instance of the Game class will inherit whatever methods the BoardGame and LogicGame classes have.
- a generic object class with iterable parameter fields
- a generic object class with non-iterable named fields
- a tuple subclass with non-iterable parameter fields
- a tuple subclass with iterable named fields
-
&&
-
=
-
==
-
||
fruit_info = {
'fruit': 'apple',
'count': 2,
'price': 3.5
}
-
fruit_info ['price'] = 1.5
-
my_list [3.5] = 1.5
-
1.5 = fruit_info ['price]
-
my_list['price'] == 1.5
5 != 6
-
yes
-
False
-
True
-
None
- The
__init__
method makes classes aware of each other if more than one class is defined in a single code file. - The
__init__
method is included to preserve backwards compatibility from Python 3 to Python 2, but no longer needs to be used in Python 3. - The
__init__
method is a constructor method that is called automatically whenever a new object is created from a class. It sets the initial state of a new object. - The
__init__
method initializes any imports you may have included at the top of your file.
-
How many microprocessors it would take to run your code in less than one second
-
How many lines of code are in your code file
-
The amount of space taken up in memory as a function of the input size
-
How many copies of the code file could fit in 1 GB of memory
-
fruit_info = {'fruit': 'apple', 'count': 2, 'price': 3.5}
-
fruit_info =('fruit': 'apple', 'count': 2,'price': 3.5 ).dict()
-
fruit_info = ['fruit': 'apple', 'count': 2,'price': 3.5 ].dict()
-
fruit_info = to_dict('fruit': 'apple', 'count': 2, 'price': 3.5)
Q49. What is the proper way to write a list comprehension that represents all the keys in this dictionary?
fruits = {'Apples': 5, 'Oranges': 3, 'Bananas': 4}
-
fruit_names = [x in fruits.keys() for x]
-
fruit_names = for x in fruits.keys() *
-
fruit_names = [x for x in fruits.keys()]
-
fruit_names = x for x in fruits.keys()
-
backtracking
-
divide and conquer
-
dynamic programming
-
decrease and conquer
Q51. What is the purpose of the self
keyword when defining or calling methods on an instance of an object?
-
self
refers to the class that was inherited from to create the object usingself
. - There is no real purpose for the
self
method. It's just legacy computer science jargon that Python keeps to stay consistent with other programming languages. -
self
means that no other arguments are required to be passed into the method. -
self
refers to the instance whose method was called.
- A class method is a regular function that belongs to a class, but it must return None.
- A class method can modify the state of the class, but they can't directly modify the state of an instance that inherits from that class.
- A class method is similar to a regular function, but a class method doesn't take any arguments.
- A class method hold all of the data for a particular class.
- You did not use very many advanced computer programming concepts in your code.
- The difficulty level your code is written at is not that high.
- It will take your program less than half a second to run.
- The amount of time it takes the function to complete grows linearly as the input size increases.
-
def getMaxNum(list_of_nums): # body of function goes here
-
func get_max_num(list_of_nums): # body of function goes here
-
func getMaxNum(list_of_nums): # body of function goes here
-
def get_max_num(list_of_nums): # body of function goes here
- in camel case without using underscores to separate words -- e.g.
maxValue = 255
- in lowercase with underscores to separate words -- e.g.
max_value = 255
- in all caps with underscores separating words -- e.g.
MAX_VALUE = 255
- in mixed case without using underscores to separate words -- e.g.
MaxValue = 255
- A deque adds items to one side and remove items from the other side.
- A deque adds items to either or both sides, but only removes items from the top.
- A deque adds items at either or both ends, and remove items at either or both ends.
- A deque adds items only to the top, but remove from either or both sides.
-
myset = {0, 'apple', 3.5}
-
myset = to_set(0, 'apple', 3.5)
-
myset = (0, 'apple', 3.5).to_set()
-
myset = (0, 'apple', 3.5).set()
- [ ]
class __init__(self):
pass
- [ ]
def __init__():
pass
- [ ]
class __init__():
pass
- [x]
def __init__(self):
pass
- A class method holds all of the data for a particular class.
- A class method can modify the state of the class, but it cannot directly modify the state of an instance that inherits from that class.
- A class method is a regular function that belongs to a class, but it must return
None
- A class method is similar to a regular function, but a class method does not take any arguments.
Q60. Which of the following is TRUE About how numeric data would be organised in a binary Search tree?
- For any given Node in a binary Search Tree, the child node to the left is less than the value of the given node and the child node to its right is greater than the given node.
- Binary Search Tree cannot be used to organize and search through numeric data, given the complication that arise with very deep trees.
- The top node of the binary search tree would be an arbitrary number. All the nodes to the left of the top node need to be less than the top node's number, but they don't need to ordered in any particular way.
- The smallest numeric value would go in the top most node. The next highest number would go in its left child node, the the next highest number after that would go in its right child node. This pattern would continue until all numeric values were in their own node.
- A decorator is similar to a class and should be used if you are doing functional programming instead of object oriented programming.
- A decorator is a visual indicator to someone reading your code that a portion of your code is critical and should not be changed.
- You use the decorator to alter the functionality of a function without having to modify the functions code.
- An import statement is preceded by a decorator, python knows to import the most recent version of whatever package or library is being imported.
- Only in some situations, as loops are used only for certain type of programming.
- When you need to check every element in an iterable of known length.
- When you want to minimize the use of strings in your code.
- When you want to run code in one file for a function in another file.
Q63. What is the most self-descriptive way to define a function that calculates sales tax on a purchase?
- [ ]
def tax(my_float):
'''Calculates the sales tax of a purchase. Takes in a float representing the subtotal as an argument and returns a float representing the sales tax.'''
pass
- [ ]
def tx(amt):
'''Gets the tax on an amount.'''
- [ ]
def sales_tax(amount):
'''Calculates the sales tax of a purchase. Takes in a float representing the subtotal as an argument and returns a float representing the sales tax.'''
- [x]
def calculate_sales_tax(subtotal):
pass
Q64. What would happen if you did not alter the state of the element that an algorithm is operating on recursively?
- You do not have to alter the state of the element the algorithm is recursing on.
- You would eventually get a KeyError when the recursive portion of the code ran out of items to recurse on.
- You would get a RuntimeError: maximum recursion depth exceeded.
- The function using recursion would return None.
- The runtime for searching in a binary search tree is O(1) because each node acts as a key, similar to a dictionary.
- The runtime for searching in a binary search tree is O(n!) because every node must be compared to every other node.
- The runtime for searching in a binary search tree is generally O(h), where h is the height of the tree.
- The runtime for searching in a binary search tree is O(n) because every node in the tree must be visited.
- You use a
mixin
to force a function to accept an argument at runtime even if the argument wasn't included in the function's definition. - You use a
mixin
to allow a decorator to accept keyword arguments. - You use a
mixin
to make sure that a class's attributes and methods don't interfere with global variables and functions. - If you have many classes that all need to have the same functionality, you'd use a
mixin
to define that functionality.
- Add items to a stack in O(1) time and remove items from a stack on O(n) time.
- Add items to a stack in O(1) time and remove items from a stack in O(1) time.
- Add items to a stack in O(n) time and remove items from a stack on O(1) time.
- Add items to a stack in O(n) time and remove items from a stack on O(n) time.
- a stacks adds items to one side and removes items from the other side.
- a stacks adds items to the top and removes items from the top.
- a stacks adds items to the top and removes items from anywhere in the stack.
- a stacks adds items to either end and removes items from either end.
- A base case is the condition that allows the algorithm to stop recursing. It is usually a problem that is small enough to solve directly.
- The base case is summary of the overall problem that needs to be solved.
- The base case is passed in as an argument to a function whose body makes use of recursion.
- The base case is similar to a base class, in that it can be inherited by another object.
Q70. Why is it considered good practice to open a file from within a Python script by using the with
keyword?
- The
with
keyword lets you choose which application to open the file in. - The
with
keyword acts like afor
loop, and lets you access each line in the file one by one. - There is no benefit to using the
with
keyword for opening a file in Python. - When you open a file using the
with
keyword in Python, Python will make sure the file gets closed, even if an exception or error is thrown.
- Virtual environments create a "bubble" around your project so that any libraries or packages you install within it don't affect your entire machine.
- Teams with remote employees use virtual environments so they can share code, do code reviews, and collaborate remotely.
- Virtual environments were common in Python 2 because they augmented missing features in the language. Virtual environments are not necessary in Python 3 due to advancements in the language.
- Virtual environments are tied to your GitHub or Bitbucket account, allowing you to access any of your repos virtually from any machine.
- python3 -m doctest
- python3
- python3 rundoctests
- python3 doctest
- any function that makes use of scientific or mathematical constants, often represented by Greek letters in academic writing
- a function that get executed when decorators are used
- any function whose definition is contained within five lines of code or fewer
- a small, anonymous function that can take any number of arguments but has only expression to evaluate
Explanation:
the lambda notation is basically an anonymous function that can take any number of arguments with only single expression (i.e, cannot be overloaded). It has been introducted in other programming languages, such as C++ and Java. The lambda notation allows programmers to "bypass" function declaration.
- You can access a specifc element in a list by indexing to its position, but you cannot access a specific element in a tuple unless you iterate through the tuple
- Lists are mutable, meaning you can change the data that is inside them at any time. Tuples are immutable, meaning you cannot change the data that is inside them once you have created the tuple.
- Lists are immutable, meaning you cannot change the data that is inside them once you have created the list. Tuples are mutable, meaning you can change the data that is inside them at any time.
- Lists can hold several data types inside them at once, but tuples can only hold the same data type if multiple elements are present.
- Static methods can be bound to either a class or an instance of a class.
- Static methods can access and modify the state of a class or an instance of a class.
- Static methods serve mostly as utility or helper methods, since they cannot access or modify a class's state.
- Static methods are called static because they always return None.
- None
- An iterable object
- A linked list data structure from a non-empty list
- All the keys of the given dictionary
- Instance attributes can be changed, but class attributes cannot be changed
- Class attributes are shared by all instances of the class. Instance attributes may be unique to just that instance
- There is no difference between class attributes and instance attributes
- Class attributes belong just to the class, not to instance of that class. Instance attributes are shared among all instances of a class
- [ ]
def get_next_card():
# method body goes here
- [x]
def get_next_card(self):
# method body goes here
- [ ]
def self.get_next_card():
# method body goes here
- [ ]
def self.get_next_card(self):
# method body goes here
- A set is an ordered collection of non-unique items. A list is an unordered collection of unique items.
- A set is an ordered collection of unique items. A list is an unordered collection of non-unique items.
- Elements can be retrieved from a list but they cannot be retrieved from a set.
- A set is an unordered collection of unique items. A list is an ordered collection of non-unique items.
- get_max_num([57, 99, 31, 18])
- call.(get_max_num)
- def get_max_num([57, 99, 31, 18])
- call.get_max_num([57, 99, 31, 18])
-
-- This is a comment
-
# This is a comment
-
/_ This is a comment _\
-
// This is a comment
- orange = my_list[1]
- my_list[1] = 'orange'
- my_list['orange'] = 1
- my_list[1] == orange
Q83. What will happen if you use a while loop and forget to include logic that eventually causes the while loop to stop?
- Nothing will happen; your computer knows when to stop running the code in the while loop.
- You will get a KeyError.
- Your code will get stuck in an infinite loop.
- You will get a WhileLoopError.
- A queue adds items to either end and removes items from either end.
- A queue adds items to the top and removes items from the top.
- A queue adds items to the top, and removes items from anywhere in, a list.
- A queue adds items to the top and removes items from anywhere in the queue.
- [x]
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
elif num_people > 4:
print("There are some people in the pool.")
else:
print("There is no one in the pool.")
- [ ]
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
if num_people > 4:
print("There are some people in the pool.")
else:
print("There is no one in the pool.")
- [ ]
num_people = 5
if num_people > 10;
print("There is a lot of people in the pool.")
elif num_people > 4;
print("There are some people in the pool.")
else;
print("There is no one in the pool.")
- [ ]
if num_people > 10;
print("There is a lot of people in the pool.")
if num_people > 4;
print("There are some people in the pool.")
else;
print("There is no one in the pool.")
This question seems to be an updated version of Question 19.