PCAP Practice Exam 3 — 20 Questions

Question 1

A programmer writes a class with a method that should be called on the class itself, not on instances. Which decorator is appropriate?

Accepted Answer

@classmethod. The @classmethod decorator transforms a method so that it receives the class itself as the first implicit argument (cls), rather than an instance (self). This allows the method to be called on the class directly, e.g., MyClass.my_method(), and is the correct choice when a method should operate on the class level, not on instances.

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@property

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@abstractmethod

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@staticmethod

Question 2

A developer needs to count the number of occurrences of the substring 'is' in the string 'This is a test. Is this a test?'. Which code correctly performs the count?

Accepted Answer

'This is a test. Is this a test?'.count('is'). Python's string method `count(substring)` returns the number of non-overlapping occurrences of the substring in the string. In 'This is a test. Is this a test?', 'is' appears twice (in 'This' and 'is'), and the method counts them correctly, ignoring case sensitivity (the capitalized 'Is' is not counted).

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'This is a test. Is this a test?'.split().count('is')

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'This is a test. Is this a test?'.index('is')

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'This is a test. Is this a test?'.find('is')

Question 3

A class has a class attribute that is a list. A developer modifies this list via one instance, and the change is reflected in all other instances. What is the best practice to avoid this unintended sharing?

Accepted Answer

Initialize the list in `__init__` rather than as a class attribute.. Class attributes are shared across all instances. By initializing the list inside `__init__`, each instance gets its own independent list object, preventing unintended mutation from affecting other instances. This is the standard Python pattern for instance-specific mutable data.

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Use a tuple instead of a list.

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Use a class method to modify the list.

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Use `deepcopy` when accessing the list.

Question 4

A senior developer in a team argues that using try-except blocks is slower than checking conditions with if statements. They propose replacing all try blocks that handle file I/O errors with existence checks using os.path.exists before opening files. During a code review, you recall that Python's official documentation and best practices prefer EAFP (Easier to Ask for Forgiveness than Permission) over LBYL (Look Before You Leap) in many cases, especially in concurrent environments. The team's application is a multi-threaded web server that serves static files from a shared directory. Which is the strongest counterargument against the senior developer's proposal?

Accepted Answer

LBYL leads to race conditions in concurrent code because the file's state can change between the check and the use.. In a multi-threaded web server, the LBYL approach (checking with os.path.exists) introduces a classic TOCTOU (Time of Check, Time of Use) race condition: between the existence check and the actual file open, another thread could delete or rename the file, causing the open to fail despite the check passing. Python's EAFP idiom (try-except) avoids this window by attempting the operation directly and handling the exception if it fails, which is inherently atomic with respect to the file system state. This is why official Python documentation recommends EAFP over LBYL in concurrent environments.

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if statements are harder to read and maintain.

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try-except can catch multiple exception types more cleanly.

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try-except blocks have no performance cost at all.

Question 5

In Python, if you have a try block followed by an except clause that catches all exceptions, which of the following is true about the else clause?

Accepted Answer

The else clause runs only if no exception is raised in the try block.. In Python, the `else` clause in a `try` statement executes only if no exception was raised in the `try` block. This is true regardless of whether the `except` clause catches all exceptions (e.g., bare `except:` or `except Exception:`). The `else` block is specifically designed for code that should run only when the `try` block completes successfully without any exception.

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The else clause runs only if an exception occurs.

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The else clause runs before the finally block regardless of exceptions.

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The else clause is used to specify additional exception handlers.

Question 6

Which of the following demonstrates that strings are immutable?

Accepted Answer

s[0] = 'J' results in a TypeError. Attempting to assign a new character to an index of a string (e.g., s[0] = 'J') raises a TypeError, which directly demonstrates that strings are immutable in Python. Immutability means the object's value cannot be changed after creation; any operation that appears to modify a string actually creates a new string object.

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s.upper() changes s in place

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s += '!' modifies s

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s.replace('a','b') modifies s

Question 7

A developer needs to parse a log file where each line contains a timestamp followed by a message. The timestamp format is 'YYYY-MM-DD HH:MM:SS'. Which string method is most appropriate to split the timestamp from the message?

Accepted Answer

str.split(). Str.split(), is the most appropriate because it splits a string on whitespace by default. Although the timestamp 'YYYY-MM-DD HH:MM:SS' contains a space, using split() without arguments returns a list of all space-separated elements. Since the timestamp is always the first two elements (date and time), the developer can join them with a space to get the full timestamp. Alternatively, split() can be used with a specified separator and maxsplit to achieve the desired split. This flexibility makes str.split() the best choice among the given options.

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str.rsplit()

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str.splitlines()

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str.partition()

Question 8

A developer wants a class 'LoggedDict' that behaves like a dict but logs all attribute access in the console. Which method override correctly implements this for getting an attribute?

Accepted Answer

def __getattribute__(self, name): print(f'Access {name}'); return super().__getattribute__(name). `__getattribute__` is the universal method called for every attribute access on an object. By overriding it, the developer can log the attribute name before delegating to the superclass implementation via `super().__getattribute__(name)`, which preserves the normal attribute lookup chain. This ensures that all attribute accesses (including those that exist and those that don't) are logged, which is the requirement for 'LoggedDict'.

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def __get__(self, instance, owner): print(f'Access'); return self

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def __getattr__(self, name): print(f'Access {name}'); return self.__dict__[name]

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def __getitem__(self, key): print(f'Access {key}'); return dict.__getitem__(self, key)

Question 9

A function receives a file path like '/home/user/docs/file.txt' and needs to return the path without the file extension, e.g., '/home/user/docs/file'. Which code reliably removes only the last dot extension, even if the directory names contain dots?

Accepted Answer

path.rsplit('.', 1)[0]. `rsplit('.', 1)` splits the string from the right, limiting the split to exactly one occurrence, which isolates the file extension (the part after the last dot) and returns everything before it. This reliably removes only the last dot extension, even if directory names contain dots, because it targets the final dot in the path.

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path.split('.')[0]

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path.replace('.', '', 1)

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path[:path.find('.')]

Question 10

Refer to the exhibit. A developer is writing a script to read this JSON configuration file. The script should write the logging configuration to a separate file called 'logging.conf'. Which file mode should be used to create the file if it doesn't exist, and overwrite it if it does?

Accepted Answer

'w'. ('w') is correct because the 'w' mode opens a file for writing, truncating it first if it exists, and creating it if it does not. This matches the requirement to overwrite an existing 'logging.conf' file or create a new one if absent.

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'x'

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'r+'

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'a'

Question 11

A developer tries to modify a string: s = 'hello'; s[0] = 'H'. What happens when this code runs?

Accepted Answer

It raises a TypeError: 'str' object does not support item assignment. Strings in Python are immutable, meaning their contents cannot be changed after creation. Attempting to assign a new character to an index position (e.g., s[0] = 'H') raises a TypeError: 'str' object does not support item assignment. To modify a string, you must create a new string using slicing or concatenation.

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It changes the string to 'Hello'

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It creates a new string 'Hello' and assigns it to s

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It raises an IndexError because index 0 is out of range

Question 12

You are a DevOps engineer managing a Python application that consists of multiple microservices. One microservice, 'data_processor', imports a shared library 'common_lib' which is also used by other microservices. The shared library is developed in a separate repository and is installed via pip in each microservice's virtual environment as an editable package (pip install -e). Recently, you updated 'common_lib' with new functions, but when you redeploy 'data_processor' (by restarting the container), the new functions are not available; the old version is still used. The container uses a Docker image built from a requirements file that specifies 'common_lib' from a Git repository. You verify that the Git commit hash in the requirements file points to the latest version. What is the most likely cause and what is the correct course of action?

Accepted Answer

Update the commit hash or use a version tag that points to the latest, and rebuild the Docker image without using cache (--no-cache).. When a Docker image is built, pip installs the package from the Git repository at the commit hash specified in the requirements file. Even if the requirements file points to the latest commit, Docker's layer caching may reuse a previously built layer that contains the old version of the package. Rebuilding the image with --no-cache forces Docker to re-execute the pip install step, fetching the latest code from Git and installing the updated common_lib. Simply restarting the container does not rebuild the image, so the old installed package persists.

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Add the common_lib source directory to sys.path in the microservice code.

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Rename the package in the requirements file to force a fresh install.

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Change the Python interpreter to a different version.

Question 13

A developer is building a logging system that writes logs to a file. The system should handle disk-full situations gracefully without crashing the main application. Which approach is appropriate?

Accepted Answer

Wrap the log write in a try/except that catches OSError and writes to stderr as fallback.. It uses a targeted try/except block around only the log write operation, catching OSError (which includes disk-full conditions) and falling back to stderr. This prevents the main application from crashing while still reporting the error, adhering to the principle of handling exceptions at the point where they occur and only when you can meaningfully recover.

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Check disk space before each write; if low, skip logging.

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Wrap the entire application in a try/except that catches all exceptions.

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Let the OSError propagate to the main program's exception handler.

Question 14

A Python script fails with 'ModuleNotFoundError: No module named 'myapp.config''. The environment variable PYTHONPATH is not set. Which of the following is the most likely cause?

Accepted Answer

The module is located in a directory not included in sys.path. When PYTHONPATH is not set, Python relies solely on sys.path to locate modules. sys.path includes the script's directory, standard library paths, and site-packages. If 'myapp.config' is not in any of these directories, Python raises ModuleNotFoundError. Option A correctly identifies that the module is in a directory not included in sys.path.

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The module's __init__.py is missing

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The module is installed in a different Python version's site-packages

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The module has a syntax error

Question 15

A package 'pkg' is installed as an egg-link in development mode. Inside the package, there is a module 'submod.py' that uses relative imports. When a developer modifies 'submod.py', they find that changes are not always reflected on import. What is the most likely reason?

Accepted Answer

Python's module caching in sys.modules prevents re-loading the modified source.. Python caches imported modules in `sys.modules`. When a module is imported, Python stores the module object in `sys.modules` and subsequent imports retrieve it from this cache without re-executing the module's code. Modifying the source file of `submod.py` does not automatically invalidate this cache, so the changes are not reflected unless the module is explicitly reloaded (e.g., with `importlib.reload()`) or the interpreter is restarted.

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The sys.path is altered by the egg-link, causing a different module to be loaded.

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Relative imports are cached in the __init__.py file.

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The __pycache__ directory is not cleared automatically.

Question 16

A programmer uses a class method to create an alternative constructor for a `Point` class. The method should parse a string like "10,20" and return a `Point` instance with x=10, y=20. Which code snippet correctly implements this?

Accepted Answer

`@classmethod
def from_string(cls, s):
    parts = s.split(',')
    return cls(int(parts[0]), int(parts[1]))`. It uses the `@classmethod` decorator, which automatically passes the class (`cls`) as the first argument. This allows the method to create an instance of the class using `cls(...)`, making it a proper alternative constructor that works correctly even if the class is subclassed. The method parses the string "10,20" by splitting on the comma and converting the parts to integers.

Answer

`def from_string(self, s):
    parts = s.split(',')
    return Point(int(parts[0]), int(parts[1]))`

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`@staticmethod
def from_string(s):
    parts = s.split(',')
    return Point(int(parts[0]), int(parts[1]))`

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`def from_string(cls, s):
    parts = s.split(',')
    return cls(int(parts[0]), int(parts[1]))`

Question 17

A developer has a module 'config.py' with the following content:

# config.py
import os
DATABASE_URL = os.getenv('DATABASE_URL', 'localhost')

Another module 'app.py' imports config and uses DATABASE_URL. During testing, the environment variable is set correctly, but the import still uses the default value 'localhost'. What is the most likely reason?

Accepted Answer

Python caches modules; config.py was imported earlier without the environment variable, and the cached version is reused.. Python caches imported modules in `sys.modules`. If `config.py` was imported earlier in the test session (e.g., during test discovery or another import) before the environment variable `DATABASE_URL` was set, the cached module would retain the default value `'localhost'`. Subsequent imports, even after setting the environment variable, reuse the cached module, so `os.getenv('DATABASE_URL', 'localhost')` is not re-evaluated.

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The import statement in app.py is placed inside a function, so it is not executed.

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The module was imported using 'from config import DATABASE_URL' which creates a separate copy.

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The environment variable is only read when the function is called, not at import time.

Question 18

Given package structure: pack/__init__.py, pack/subpack/__init__.py, pack/subpack/mod.py. Inside pack/__init__.py, which import statement correctly imports mod.py using a relative import?

Accepted Answer

from .subpack import mod. `from .subpack import mod` uses a leading dot to indicate a relative import from the current package (`pack`), then navigates into `subpack` and imports `mod`. This is the proper syntax for importing a module from a subpackage within the same parent package.

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from . import subpack.mod

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from subpack import mod

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from ..subpack import mod

Question 19

Drag and drop the steps to serialize a Python object to JSON using the json module into the correct order.

Accepted Answer

Step 1: Import the json module. Step 2: Create a Python dictionary. Step 3: Open a file in write mode. Step 4: Use json.dump() to write the dictionary to the file.. Serialization to JSON involves importing json, preparing data, using dumps for string or dump for file output.

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Step 1: Create a Python dictionary. Step 2: Import the json module. Step 3: Open a file in write mode. Step 4: Use json.dump() to write the dictionary to the file.

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Step 1: Import the json module. Step 2: Open a file in write mode. Step 3: Create a Python dictionary. Step 4: Use json.dump() to write the dictionary to the file.

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Step 1: Import the json module. Step 2: Create a Python dictionary. Step 3: Use json.dump() to write the dictionary to the file. Step 4: Open a file in write mode.

Question 20

A developer writes a class 'Logger' with a class method 'log(msg)' that writes to a file. Another class 'AppLogger' inherits from 'Logger'. The developer expects both classes to share the same file handle. However, after creating an instance of 'AppLogger', the file handle is different. What is the most likely cause?

Accepted Answer

The file handle is opened in the __init__ method of the base class. If the file handle is opened in the `__init__` method of the base class, each time a new instance is created (including when an `AppLogger` instance is created), a new file handle is opened. This means the `Logger` class and the `AppLogger` class do not share the same file handle; instead, each instance gets its own handle. To share a single file handle across all instances, the file handle should be opened as a class attribute or in a class method, not in `__init__`.

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The 'log' method is defined as a class method using @classmethod

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The file handle is stored as a private attribute __file

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The subclass overrides the 'log' method