PCAP Practice Test 27 — 15 Questions

Question 1

A company runs a data processing pipeline that reads CSV files from a network drive. Occasionally, the network drive is unreachable causing FileNotFoundError. The current code uses a bare except clause that catches all exceptions, which also masks programming errors like NameError. The lead developer wants to implement a more robust exception handling strategy. The requirement is to log the specific error (including the filename) and retry the operation up to 3 times with a 2-second delay between retries. If after 3 attempts the file is still inaccessible, the pipeline should skip the file and continue with the next one. Which approach best meets these requirements?

Accepted Answer

Use a try-except block catching OSError, with a retry loop that logs the error and continues after max retries.. Option B is correct because it catches OSError, which is the parent class for file-related errors like FileNotFoundError, while still allowing programming errors like NameError to propagate. The retry loop with a 2-second delay and a maximum of 3 attempts satisfies the requirement, and after exhausting retries, the loop naturally continues to the next file, skipping the problematic one.

Answer

Use os.path.exists before opening the file, and if it returns False, log and skip.

Answer

Use a try-except block catching Exception, with a retry loop and logging.

Answer

Use a with statement; if an exception occurs, call the function recursively up to 3 times.

Question 2

A programmer needs to write a function that reads a large binary file (over 1 GB) and returns its entire content as a bytes object. The function must handle the case where the path provided is actually a directory (IsADirectoryError) by returning an empty bytes object. Which implementation is most memory-efficient and correctly handles the directory case? Consider that reading the entire file into memory is acceptable for the business case.

Accepted Answer

Use 'with open(path, 'rb') as f: return f.read()' inside a try-except IsADirectoryError catch that returns b''.. Option A is correct because it uses a try-except block specifically catching `IsADirectoryError`, which is raised when `open()` is called on a directory path. The `with` statement ensures the file is properly closed, and `f.read()` returns the entire content as a bytes object, which is memory-efficient for this use case (the file is read in one large allocation, avoiding overhead from chunking).

Answer

Use 'with open(path, 'rb') as f: return f.read()' inside a try-except OSError that returns b''.

Answer

Use os.path.isfile(path) to check before opening; if not a file, return b''.

Answer

Use 'with open(path, 'rb') as f: return [chunk for chunk in iter(lambda: f.read(1024), b'')]' inside a try-except IsADirectoryError.

Question 3

A company has a large Python application that uses multiple packages from different directories. The application's main entry point is at /opt/app/main.py. There is a package 'common' located at /opt/app/common/ and another package 'services' at /opt/app/services/. Both packages have __init__.py files. Additionally, there is a third-party package 'utils' installed in the system site-packages. Recently, a developer added a new module 'helpers.py' to the 'common' package. When trying to import 'common.helpers' from a script inside 'services', an ImportError is raised: 'No module named common.helpers'. However, importing 'common' itself works. The sys.path includes /opt/app/ and the site-packages. What is the most likely cause of the import failure?

Accepted Answer

There is another 'common' package elsewhere in sys.path that shadows the intended one, and the shadowed package does not have a 'helpers' submodule.. Option B is correct because the most likely cause is that a different 'common' package (without a 'helpers' submodule) appears earlier in sys.path and shadows the intended /opt/app/common/ package. Since sys.path includes /opt/app/ and site-packages, if a 'common' package exists in site-packages or another directory listed before /opt/app/, Python will import that shadowed package instead, and it lacks the newly added 'helpers' module. This explains why importing 'common' succeeds (the shadowed package exists) but 'common.helpers' fails.

Answer

The 'helpers.py' file was added after the Python interpreter started, and sys.modules caching prevents new imports.

Answer

The PYTHONPATH environment variable is not set, so the /opt/app/ directory is not searched.

Answer

The 'common' package itself is already imported and cached, so adding a new module does not become visible.

Question 4

An application uses a class to represent a configuration object that reads settings from a file. The class has a class attribute config_cache that holds a dictionary of loaded configurations to avoid repeated file reads. However, the developer notices that when they modify the dictionary for one instance, it affects all instances. They want to ensure that each instance has its own copy of the configuration data upon initialization. Which change should they make?

Accepted Answer

Move the dictionary initialization into the __init__ method so each instance creates its own dictionary.. Option A is correct because moving the dictionary initialization into the __init__ method ensures that each instance gets its own separate dictionary object. Class attributes are shared across all instances, so modifying the dictionary via one instance changes it for all. By assigning `self.config_cache = {}` inside __init__, each instance creates a new, independent dictionary upon instantiation, solving the shared-state problem.

Answer

Use a @staticmethod to return a new dictionary each time.

Answer

Keep the class attribute but use a deep copy in __init__ before modifying.

Answer

Define the dictionary inside a class method.

Question 5

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.. Option D is correct because 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.

Answer

if statements are harder to read and maintain.

Answer

try-except can catch multiple exception types more cleanly.

Answer

try-except blocks have no performance cost at all.

Question 6

You maintain a Python library 'myutils' that is installed as a package in the system. The library has a submodule 'config' that reads configuration from a file. Recently, a user reported that after updating the library, their application still uses the old configuration values. They confirmed that the config file on disk has been updated. The library's __init__.py does: from .config import load_config. The user's application imports load_config from myutils and calls it each time they need configuration. What is the most likely cause of the issue?

Accepted Answer

The library's .pyc files were not regenerated because the .py timestamps were not updated during the install, so Python used the cached bytecode from the previous version.. Option C is correct because Python caches compiled bytecode in .pyc files. If the .pyc file's timestamp is newer than the corresponding .py file, Python will use the cached bytecode without recompiling. During a package update, if the .py files' timestamps are not updated (e.g., due to a flawed installation process), Python continues to load the old .pyc, causing the old configuration-reading code to execute even though the config file on disk has changed.

Answer

The user did not restart the Python interpreter, so sys.modules still contains the old module.

Answer

The import statement in __init__.py is cached, so the module is not reloaded even after update.

Answer

The config module caches the configuration file contents in memory after the first read.

Question 7

A data scientist writes a script to parse a configuration file that contains lines in 'key=value' format. Some lines may have malformed data that cause a ValueError when trying to convert the value to an integer. The requirement is to process all valid lines and skip any that cause a ValueError, but continue processing subsequent lines. The script should log a warning for each skipped line, including the line number. Which implementation correctly fulfills this requirement? (Assume the file is opened with 'with open(...) as f'.)

Accepted Answer

Inside a for loop over f, wrap the parsing in a try-except ValueError block, log the error, and continue.. Option A is correct because it uses a try-except ValueError block inside the for loop, which catches the specific exception when converting the value to an integer, logs a warning with the line number, and continues to the next line. This ensures all valid lines are processed while malformed lines are skipped without halting execution, fulfilling the requirement exactly.

Answer

Wrap the entire file processing in a try-except ValueError block; if an error occurs, log and break.

Answer

Before parsing, check if the value string consists of digits using str.isdigit; if not, skip the line.

Answer

Inside a for loop over f, wrap the parsing in a try-except Exception block, log the error, and continue.

Question 8

You are working on a legacy system that processes financial transactions. The system uses a class hierarchy: Transaction (base), Deposit, Withdrawal, Transfer. Each subclass overrides a method 'process()' to handle its specific logic. The code often runs in a multi-threaded environment and you notice intermittent errors where a transaction is processed twice. The logging shows that the same transaction object is being passed to the process method multiple times. The transaction objects are created from a factory function that caches recently used transactions. The errors seem to occur when two threads call the factory at the same time with the same parameters. After investigating, you find that the factory uses a class-level dictionary to cache objects. Which of the following is the most appropriate solution to prevent double processing?

Accepted Answer

Make the process() method idempotent by checking if the transaction has already been applied to the account (e.g., check balance changes). Option D is correct because the core issue is that the same transaction object can be processed multiple times in a multi-threaded environment, even if the factory is fixed. Making process() idempotent by checking whether the transaction has already been applied (e.g., verifying account balance changes) ensures that repeated calls with the same object do not cause duplicate financial effects, directly addressing the symptom of double processing regardless of how the object is cached or retrieved.

Answer

Add a lock around the cache lookup and creation in the factory function

Answer

Add a flag to each transaction object to indicate if it has been processed, and check it at the start of process()

Answer

Remove the caching mechanism from the factory function to ensure new objects are always created

Question 9

You are a developer at a company that processes customer feedback. Each feedback entry is stored as a string containing a rating (1-5) followed by a colon and then the comment. For example: '4: Great service'. You need to extract only the comments from feedback that have a rating of 4 or 5. You have a list of feedback strings. Which code snippet correctly implements this?

Accepted Answer

[s.split(':')[1].strip() for s in feedback if s.split(':')[0].strip() in ('4','5')]. Option C is correct because it splits each feedback string on ':', extracts the comment (index [1]), strips whitespace, and filters only those entries where the rating (index [0], stripped) is exactly '4' or '5'. This ensures only comments from high-rated feedback are collected, handling potential spaces around the colon.

Answer

[s for s in feedback if s.startswith('4') or s.startswith('5')]

Answer

[s.split(':') for s in feedback][1]

Answer

[s.split(':')[1] for s in feedback if '4' in s or '5' in s]

Question 10

You are working on a Python-based data processing pipeline that runs on a Linux server. The pipeline consists of several custom packages and modules located in /opt/mypipeline. The directory structure includes:

/opt/mypipeline/
    __init__.py
    core/
        __init__.py
        processor.py
    utils/
        __init__.py
        logger.py

The main entry point is /opt/mypipeline/run.py, which imports modules from core and utils. The pipeline is executed using the command:

python /opt/mypipeline/run.py

Recently, the system administrator added a new Python package 'external_lib' to /opt/external_lib, and updated the PYTHONPATH environment variable to include /opt/external_lib. However, after this change, the pipeline fails to start with the following error:

ImportError: cannot import name 'process' from 'core.processor' (unknown location)

You check the PYTHONPATH and find it contains:

/opt/mypipeline:/opt/external_lib

The 'core.processor' module defines a function 'process' that is imported in run.py as:

from core.processor import process

The 'core' package has an __init__.py file. The 'external_lib' package also has a subpackage named 'core' with an __init__.py. What is the most likely cause of the import error?

Accepted Answer

The 'external_lib' package contains a 'core' subpackage that shadows the 'core' package from mypipeline, and Python is resolving to the wrong one.. The most likely cause is that the 'external_lib' package contains a 'core' subpackage, which shadows the 'core' package from mypipeline. When Python resolves the import 'from core.processor import process', it searches the directories listed in PYTHONPATH in order. Since PYTHONPATH includes both /opt/mypipeline and /opt/external_lib, and the 'core' subpackage in external_lib is found first (or its presence causes a conflict), Python may resolve to the wrong 'core' package, which does not contain a 'processor' module, leading to the ImportError.

Answer

The __init__.py file in /opt/mypipeline/core/ is missing or empty.

Answer

The import statement in run.py is incorrect; it should use 'from core.processor import process as process' or similar.

Answer

The PYTHONPATH environment variable is set incorrectly; it should include /opt/mypipeline before /opt/external_lib.

Question 11

You are a developer for an e-commerce platform. The system receives product descriptions from suppliers in various formats. One supplier sends descriptions with inconsistent capitalization, extra whitespace, and occasional leading/trailing punctuation. Your task is to write a function that normalizes these descriptions: convert to lowercase, remove leading/trailing whitespace and punctuation (.,!?;:), and replace multiple spaces with a single space. The function should return the cleaned string. Which implementation correctly performs all these steps?

Accepted Answer

def normalize(s): import re; s = s.strip(); s = s.strip('.,!?;:'); s = s.lower(); s = re.sub(r'\s+', ' ', s); return s. Option A is correct because it performs all required steps in the correct order: it first strips leading/trailing whitespace with `strip()`, then removes leading/trailing punctuation using `strip('.,!?;:')`, converts to lowercase with `lower()`, and finally replaces multiple spaces with a single space using `re.sub(r'\s+', ' ', s)`. This ensures that punctuation is removed only from the edges after whitespace is handled, and internal whitespace is normalized last.

Answer

def normalize(s): return ' '.join(s.lower().split())

Answer

def normalize(s): return s.lower().strip('.,!?;: ')

Answer

def normalize(s): return s.strip().lower()

Question 12

You are a Python developer at a financial firm. Your team maintains a high-frequency trading system that reads real-time market data from a binary file 'market_feed.bin'. The file is updated every millisecond by an external process. Your script uses a `with` statement to open the file in binary read mode and reads exactly 1024 bytes each iteration in an infinite loop. Recently, the script has been crashing intermittently with an `OSError` with message '[Errno 9] Bad file descriptor'. The error occurs at random times, sometimes after minutes, sometimes after hours. The system runs on Linux. The script runs as a daemon. You suspect the issue is related to file descriptor exhaustion or the external process manipulating the file. What is the most likely cause and the best course of action?

Accepted Answer

Use file locking (e.g., fcntl.flock) to coordinate access with the external process, and re-open the file if a lock cannot be acquired.. The most likely cause is that the external process overwrites or truncates 'market_feed.bin' between reads, causing the file descriptor held by the Python script to become invalid (stale). On Linux, when a file is replaced or truncated by another process, the original file descriptor may point to a deleted inode, and subsequent reads yield 'Bad file descriptor'. Option D is correct because file locking (e.g., fcntl.flock with LOCK_SH or LOCK_EX) coordinates access, and re-opening the file after a lock failure ensures a fresh, valid descriptor.

Answer

Open the file without using a context manager and manually close it after each read.

Answer

Increase the read buffer size to 4096 bytes to reduce the number of read operations.

Answer

Catch OSError and re-open the file within the except block, then continue reading.

Question 13

Given s = 'Python', what is s[1:4]?

Accepted Answer

'yth'. In Python, string slicing with `s[start:stop]` extracts characters from index `start` up to but not including index `stop`. Since indexing starts at 0, `s[1:4]` on 'Python' takes indices 1 ('y'), 2 ('t'), and 3 ('h'), resulting in 'yth'. Option B is correct because it exactly matches this slice.

Answer

'pyt'

Answer

'ytho'

Answer

'Pyt'

Question 14

Refer to the exhibit. What is the likely outcome of running this code?

Accepted Answer

The program raises a SyntaxError due to invalid escape sequences.. Option A is correct because the code contains backslash sequences like `
` and `	` that are not valid escape sequences in Python. Python 3.12+ raises a `SyntaxError: invalid escape sequence` by default, and in earlier versions it emits a `DeprecationWarning` that will become an error in future versions. The raw string prefix `r` or escaping the backslash (`\`) is required to avoid this.

Answer

The program runs but the file contents are incorrect.

Answer

The program raises a FileNotFoundError because the path is invalid after escape interpretation.

Answer

The file is opened successfully because backslashes are ignored.

Question 15

Given: s = "Python". Which expression raises an IndexError?

Accepted Answer

s[-10]. Option C is correct because Python strings are zero-indexed, so valid indices for 'Python' are 0 through 5. Negative indices wrap around, with -1 being the last character. Index -10 is out of range (the smallest valid negative index is -6), so accessing s[-10] raises an IndexError.

Answer

s[-1]

Answer

s[6]

Answer

s[5]