Free Practice Questions for Python Institute PCPP-32-101 Exam

In the given code snippet, the  repeat  function is a decorator that takes an argument  num_times  specifying the number of times the decorated function should be called. The  repeat  function returns an inner function  wrapper_repeat  that takes a function  func  as an argument and returns another inner function  wrapper  that calls  func   num_times  times.

The provided code snippet represents an example of a decorator that accepts its own arguments. The  @decorator_function  syntax is used to apply the  decorator_function  to the  some_function  function. The  decorator_function  takes an argument  arg1  and defines an inner function  wrapper_function  that takes the original function  func  as its argument. The  wrapper_function  then returns the result of calling  func , along with the  arg1  argument passed to the  decorator_function .

Here is an example of how to use this decorator with  some_function :

@ decorator_function ( "argument 1" )

def some_function ():

return "Hello world"

When  some_function  is called, it will first be passed as an argument to the  decorator_function . The  decorator_function  then adds the string  "argument 1"  to the result of calling  some_function()  and returns the resulting string. In this case, the final output would be  "Hello world argument 1" .

The correct answers are C and D. PEP 8 recommends surrounding top-level function and class definitions with two blank lines. This improves readability by clearly separating major program units in a module. Inside a class, method definitions should be separated by a single blank line because methods are more closely related and need less visual separation than top-level definitions. Option A is incorrect because imports are commonly grouped into standard library imports, third-party imports, and local application imports, with blank lines separating those groups. Option B is incorrect because one blank line is insufficient for top-level functions and classes under PEP 8. These spacing rules support clean, predictable, maintainable Python code layout.

Option A is true because PEP 8 recommends that function and variable names should be lowercase, with words separated by underscores .

Option D is true because PEP 8 recommends that constants should be written in all capital letters with words separated by underscores .

PEP 8 is the official style guide for Python code. It provides guidelines for how to write readable code that follows consistent naming conventions. The aim of PEP 8 is to improve the readability of Python code and make it easier to understand and maintain.

According to PEP 8, variable and function names should be written in all lower-case letters with words separated by underscores, as stated in A. Constants, which are variables whose value is expected to remain constant throughout the code, should be written in all upper-case letters with words separated by underscores, as stated in D.

The correct answers are C and D. PEP 8 recommends that module names should be short and entirely lower-case; underscores may be used only when they improve readability. This keeps imports clean and predictable. PEP 8 also warns against using the single-character variable names l, O, or I because they can be visually confused with 1 and 0 in many fonts. Option A is incorrect because Python class names should normally use the CapWords convention, not mixedCase. Option B is incorrect because exception names are class names and should follow class naming conventions; if the exception represents an error, the name should usually end with Error.

A static method is a method that belongs to a class rather than an instance of the class. It is defined using the  @staticmethod  decorator and does not take a  self  or  cls  parameter. Static methods are often used to define utility functions that do not depend on the state of an instance or the class itself.

A. It addresses a service deployed at localhost (the host where the code is run).

This statement is true because localhost is a special hostname that refers to the local machine or the current host where the code is run. It is equivalent to using the IP address 127.0.0.1, which is the loopback address of the network interface. By using localhost as the hostname, the invocation addresses a service that is deployed on the same machine as the client.

D. It addresses a service listening at port 3000.

This statement is true because port 3000 is the part of the URL that follows the colon after the hostname. It specifies the port number where the service is listening for incoming requests. A port number is a 16-bit integer that identifies a specific process or application on a host. By using port 3000, the invocation addresses a service that is listening at that port.

B. It addresses a service whose timeout is set to 3000 ms.

This statement is false because timeout is not a part of the URL, but a parameter that can be passed to the requests.get () function in Python. Timeout specifies how long to wait for the server to send data before giving up. It is measured in seconds, not milliseconds. By using timeout=3, the invocation sets the timeout to 3 seconds, not 3000 ms.

C. It addresses a service located at the following address local.host.com.

This statement is false because local.host.com is not the same as localhost. Local.host.com is a fully qualified domain name (FQDN) that consists of three parts: local, host, and com. It requires DNS resolution to map it to an IP address. Localhost, on the other hand, is a special hostname that does not require DNS resolution and always maps to 127.0.0.1. By using localhost as the hostname, the invocation does not address a service located at local.host.com.

To test whether two variables refer to the same object in memory, you should use the  is  operator. The  is  operator returns  True  if the two variables point to the same object in memory, and  False  otherwise.

For example:

a = [1, 2, 3]

b = a

c = [1, 2, 3]

print(a is b) # True

print(a is c) # False

In this example,  a  and  b  refer to the same list object in memory, so  a is b  returns  True . On the other hand,  a  and  c  refer to two separate list objects with the same values, so  a is c  returns  False .

The issue is method resolution order, commonly called MRO. In Python multiple inheritance, the interpreter must build a consistent linear inheritance path for the class. Here, B already inherits from A, meaning B must appear before A in the resolution order. However, class C(A, B) asks Python to resolve A before B. These two requirements conflict, so Python cannot create a valid MRO for class C. This results in a TypeError related to inconsistent method resolution order. The absence of an __init__() method is not a problem because Python supplies a default initializer. Also, C can inherit run() if the inheritance order is valid.

The correct answer is B because Python’s pickle module can serialize many built-in data types, including integers, floating-point numbers, complex numbers, strings, bytes, bytearrays, booleans, and None. However, it does not serialize the actual implementation or definition body of functions and classes. Top-level functions and classes may sometimes be pickled by reference using their fully qualified names, but their code definitions are not stored inside the pickle byte stream. This distinction matters: pickle records enough information to locate an object again during unpickling, but it does not preserve source code, nested definitions, lambda functions, or dynamically created definitions in a portable way. Therefore, “function and class definitions” is the option that cannot truly be pickled as definitions.