Python Advanced Coding MCQ Questions with Answers (Latest 2026)

Q1. What does `yield` do in a Python function?

A. Returns and exits permanently B. Creates a generator that can pause and resume C. Starts a new thread D. Caches results automatically

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Answer: Creates a generator that can pause and resume

Here, Creates a generator that can pause and resume is the right choice. `yield` turns a function into a generator and preserves execution state between iterations. It aligns directly with what the question asks about what does `yield` do in a python function. A quick elimination of partially true options helps confirm it.

Q2. Which method makes an object iterable?

A. `__next__` only B. `__iter__` returning an iterator C. `__len__` D. `__contains__`

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Answer: `__iter__` returning an iterator

In this case, `__iter__` returning an iterator is correct. An iterable implements `__iter__`; the returned iterator implements `__next__`. It aligns directly with what the question asks about which method makes an object iterable. A quick elimination of partially true options helps confirm it.

Q3. What exception signals iterator exhaustion?

A. `ValueError` B. `IndexError` C. `StopIteration` D. `RuntimeError`

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Answer: `StopIteration`

The best option here is `StopIteration`. Iterators raise `StopIteration` when no more items are available. It aligns directly with what the question asks about what exception signals iterator exhaustion. A quick elimination of partially true options helps confirm it.

Q4. Why use `functools.lru_cache`?

A. To enforce type hints B. To memoize function results C. To parallelize loops D. To auto-close files

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Answer: To memoize function results

For this question, To memoize function results is correct. `lru_cache` caches previous function calls for faster repeated lookups. It aligns directly with what the question asks about why use `functools.lru_cache`. A quick elimination of partially true options helps confirm it.

Q5. What is the main use of decorators?

A. Create classes dynamically B. Wrap/extend function behavior without modifying source C. Compile Python to C D. Prevent recursion

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Answer: Wrap/extend function behavior without modifying source

Wrap/extend function behavior without modifying source is the correct answer here. Decorators take a callable and return an enhanced callable. It aligns directly with what the question asks about what is the main use of decorators. A quick elimination of partially true options helps confirm it.

Q6. `@wraps` from `functools` is used to:

A. Auto-retry a function B. Preserve wrapped function metadata C. Convert sync to async D. Validate arguments

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Answer: Preserve wrapped function metadata

Here, Preserve wrapped function metadata is the right choice. `@wraps` keeps name, docstring, and other attributes of the original function. This matches the core idea being tested around `@wraps` from `functools` is used to:. A quick elimination of partially true options helps confirm it.

Q7. What does `*args` capture?

A. Keyword-only args B. Positional arguments as a tuple C. Global variables D. Return values

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Answer: Positional arguments as a tuple

In this case, Positional arguments as a tuple is correct. `*args` collects extra positional arguments into a tuple. This matches the core idea being tested around what does `*args` capture. A quick elimination of partially true options helps confirm it.

Q8. What does `**kwargs` capture?

A. Positional args as list B. Keyword arguments as a dict C. Type annotations D. Imported modules

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Answer: Keyword arguments as a dict

The best option here is Keyword arguments as a dict. `**kwargs` collects named arguments into a dictionary. This matches the core idea being tested around what does `**kwargs` capture. A quick elimination of partially true options helps confirm it.

Q9. In Python 3, list comprehensions have what scope behavior?

A. Leak loop variable globally B. Loop variable is local to comprehension C. Require global keyword D. Cannot nest

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Answer: Loop variable is local to comprehension

For this question, Loop variable is local to comprehension is correct. Comprehension variables do not leak into enclosing scope in Python 3. This matches the core idea being tested around in python 3, list comprehensions have what scope. A quick elimination of partially true options helps confirm it.

Q10. A generator expression differs from list comprehension by:

A. Always faster CPU B. Lazy evaluation C. Returning dict D. Disallowing conditions

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Answer: Lazy evaluation

Lazy evaluation is the correct answer here. Generator expressions produce items on demand, saving memory. This matches the core idea being tested around a generator expression differs from list comprehension by:. A quick elimination of partially true options helps confirm it.

Q11. What does `with` statement rely on?

A. `__enter__` and `__exit__` B. `__init__` and `__del__` C. `open` and `close` only D. `try` keyword only

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Answer: `__enter__` and `__exit__`

Here, `__enter__` and `__exit__` is the right choice. Context managers implement these methods for setup/cleanup. That is exactly the concept behind what does `with` statement rely on in this context. A quick elimination of partially true options helps confirm it.

Q12. Purpose of `contextlib.contextmanager` is to:

A. Create async loop B. Build context managers from generator functions C. Serialize objects D. Avoid exceptions

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Answer: Build context managers from generator functions

In this case, Build context managers from generator functions is correct. It allows a single `yield`-based function to define enter/exit behavior. That is exactly the concept behind purpose of `contextlib.contextmanager` is to: in this context. A quick elimination of partially true options helps confirm it.

Q13. What is a descriptor in Python?

A. A type hint B. Object defining `__get__`, `__set__`, or `__delete__` C. A module docstring D. A frozen dataclass

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Answer: Object defining `__get__`, `__set__`, or `__delete__`

The best option here is Object defining `__get__`, `__set__`, or `__delete__`. Descriptors customize managed attribute access. That is exactly the concept behind what is a descriptor in python in this context. A quick elimination of partially true options helps confirm it.

Q14. `property` is an example of:

A. Decorator only B. Descriptor C. Iterator D. Context manager

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Answer: Descriptor

For this question, Descriptor is correct. `property` implements descriptor protocol for computed attributes. That is exactly the concept behind `property` is an example of: in this context. A quick elimination of partially true options helps confirm it.

Q15. What does `__slots__` primarily do?

A. Enable multiprocessing B. Restrict instance attributes and reduce memory C. Force immutability D. Speed up imports

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Answer: Restrict instance attributes and reduce memory

Restrict instance attributes and reduce memory is the correct answer here. `__slots__` avoids per-instance `__dict__` unless explicitly included. That is exactly the concept behind what does `__slots__` primarily do in this context. A quick elimination of partially true options helps confirm it.

Q16. Metaclasses are commonly used to:

A. Control class creation behavior B. Run OS commands C. Replace inheritance D. Create async tasks

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Answer: Control class creation behavior

Here, Control class creation behavior is the right choice. Metaclasses customize how classes themselves are constructed. It fits the requirement in the prompt about metaclasses are commonly used to:. A quick elimination of partially true options helps confirm it.

Q17. `type` can be used to dynamically:

A. Delete modules B. Create classes C. Bypass GIL D. Compile bytecode

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Answer: Create classes

In this case, Create classes is correct. `type(name, bases, dict)` creates a class at runtime. It fits the requirement in the prompt about `type` can be used to dynamically:. A quick elimination of partially true options helps confirm it.

Q18. `super()` is primarily for:

A. Thread safety B. Calling methods from parent in MRO-aware way C. Skipping inheritance D. Creating static methods

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Answer: Calling methods from parent in MRO-aware way

The best option here is Calling methods from parent in MRO-aware way. `super()` follows method resolution order in multiple inheritance. It fits the requirement in the prompt about `super()` is primarily for:. A quick elimination of partially true options helps confirm it.

Q19. MRO stands for:

A. Method Resource Ordering B. Method Resolution Order C. Module Runtime Option D. Memory Reference Operation

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Answer: Method Resolution Order

For this question, Method Resolution Order is correct. MRO defines lookup path for attributes and methods. It fits the requirement in the prompt about mro stands for:. A quick elimination of partially true options helps confirm it.

Q20. What does `abc.ABC` help enforce?

A. Runtime speed B. Abstract base class contracts C. File permissions D. Dataclass defaults

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Answer: Abstract base class contracts

Abstract base class contracts is the correct answer here. Abstract methods must be implemented by concrete subclasses. It fits the requirement in the prompt about what does `abc.abc` help enforce. A quick elimination of partially true options helps confirm it.

Q21. `typing.Protocol` enables:

A. Nominal typing only B. Structural typing (duck-typed interfaces) C. Runtime bytecode patching D. C extensions

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Answer: Structural typing (duck-typed interfaces)

Here, Structural typing (duck-typed interfaces) is the right choice. Protocols define required methods/attributes without inheritance. This is the most accurate statement for `typing.protocol` enables:. A quick elimination of partially true options helps confirm it.

Q22. What does `@dataclass(frozen=True)` imply?

A. Thread-safe mutation B. Instances are immutable-like C. No `__init__` generated D. No comparison methods

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Answer: Instances are immutable-like

In this case, Instances are immutable-like is correct. Frozen dataclasses prevent field reassignment after initialization. This is the most accurate statement for what does `@dataclass(frozen=true)` imply. A quick elimination of partially true options helps confirm it.

Q23. Use `field(default_factory=list)` to avoid:

A. Syntax errors B. Mutable default argument sharing C. Type checking D. Recursion limits

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Answer: Mutable default argument sharing

The best option here is Mutable default argument sharing. `default_factory` creates a new list per instance. This is the most accurate statement for use `field(default_factory=list)` to avoid:. A quick elimination of partially true options helps confirm it.

Q24. What is the common pitfall with mutable default args in functions?

A. They are re-created each call B. They persist across calls C. They cannot be iterated D. They force keyword-only

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Answer: They persist across calls

For this question, They persist across calls is correct. Default objects are evaluated once at function definition time. This is the most accurate statement for what is the common pitfall with mutable default. A quick elimination of partially true options helps confirm it.

Q25. Best fix for mutable default arg is:

A. Use global variable B. Use `None` and initialize inside C. Use tuple always D. Disable linting

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Answer: Use `None` and initialize inside

Use `None` and initialize inside is the correct answer here. This creates a fresh object each invocation. This is the most accurate statement for best fix for mutable default arg is:. A quick elimination of partially true options helps confirm it.

Q26. `nonlocal` keyword is used to:

A. Modify variable in enclosing function scope B. Access module globals only C. Import local modules D. Create closures

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Answer: Modify variable in enclosing function scope

Here, Modify variable in enclosing function scope is the right choice. `nonlocal` binds assignments to nearest enclosing non-global scope. It aligns directly with what the question asks about `nonlocal` keyword is used to:. The other options are either incomplete or contextually incorrect.

Q27. `global` keyword affects:

A. Class attributes B. Module-level variables C. Builtins only D. Thread-local storage

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Answer: Module-level variables

In this case, Module-level variables is correct. `global` declares assignment to a module scope name. It aligns directly with what the question asks about `global` keyword affects:. The other options are either incomplete or contextually incorrect.

Q28. A closure is:

A. A file handle B. Function carrying referenced outer-scope bindings C. A class with slots D. A context manager

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Answer: Function carrying referenced outer-scope bindings

The best option here is Function carrying referenced outer-scope bindings. Closures retain access to lexical environment after outer function returns. It aligns directly with what the question asks about a closure is:. The other options are either incomplete or contextually incorrect.

Q29. `itertools.islice` is useful for:

A. Sorting dicts B. Taking slices from iterators lazily C. Creating async streams D. Type conversion

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Answer: Taking slices from iterators lazily

For this question, Taking slices from iterators lazily is correct. It slices without materializing the whole iterable. It aligns directly with what the question asks about `itertools.islice` is useful for:. The other options are either incomplete or contextually incorrect.

Q30. `itertools.chain` does what?

A. Parallel maps B. Concatenates iterables lazily C. Builds linked lists D. Encrypts strings

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Answer: Concatenates iterables lazily

Concatenates iterables lazily is the correct answer here. `chain` iterates through input iterables one after another. It aligns directly with what the question asks about `itertools.chain` does what. The other options are either incomplete or contextually incorrect.

Q31. What does `enumerate(seq, start=1)` return?

A. Only indexes B. Pairs of (index, value) C. Only values D. Dict mapping

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Answer: Pairs of (index, value)

Here, Pairs of (index, value) is the right choice. `enumerate` yields index-item tuples. This matches the core idea being tested around what does `enumerate(seq, start=1)` return. The other options are either incomplete or contextually incorrect.

Q32. `zip(a, b)` in Python 3 returns:

A. List B. Iterator C. Tuple D. Set

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Answer: Iterator

In this case, Iterator is correct. It is lazy; convert to list if materialization is needed. This matches the core idea being tested around `zip(a, b)` in python 3 returns:. The other options are either incomplete or contextually incorrect.

Q33. Why use `zip_longest` from `itertools`?

A. For equal length only B. To combine uneven iterables with fill values C. To sort zipped pairs D. To remove duplicates

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Answer: To combine uneven iterables with fill values

The best option here is To combine uneven iterables with fill values. `zip_longest` continues until the longest iterable is exhausted. This matches the core idea being tested around why use `zip_longest` from `itertools`. The other options are either incomplete or contextually incorrect.

Q34. `collections.defaultdict` helps by:

A. Auto-sorting keys B. Providing default values for missing keys C. Thread locking dict D. Immutable mapping

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Answer: Providing default values for missing keys

For this question, Providing default values for missing keys is correct. It calls a factory when accessing absent keys. This matches the core idea being tested around `collections.defaultdict` helps by:. The other options are either incomplete or contextually incorrect.

Q35. `collections.Counter` is ideal for:

A. Network sockets B. Counting hashable elements C. Binary serialization D. Regex parsing

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Answer: Counting hashable elements

Counting hashable elements is the correct answer here. Counter tallies occurrences efficiently. This matches the core idea being tested around `collections.counter` is ideal for:. The other options are either incomplete or contextually incorrect.

Q36. `deque` is often preferred over list for:

A. Random indexing B. Fast append/pop from both ends C. Sorting D. Numeric operations

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Answer: Fast append/pop from both ends

Here, Fast append/pop from both ends is the right choice. Deque provides efficient O(1)-like end operations. That is exactly the concept behind `deque` is often preferred over list for: in this context. The other options are either incomplete or contextually incorrect.

Q37. What does `heapq` implement?

A. Balanced tree B. Binary heap priority queue C. Hash map D. LRU cache

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Answer: Binary heap priority queue

In this case, Binary heap priority queue is correct. `heapq` supports push/pop of smallest element efficiently. That is exactly the concept behind what does `heapq` implement in this context. The other options are either incomplete or contextually incorrect.

Q38. In `heapq`, by default `heappop` returns:

A. Largest item B. Smallest item C. Random item D. Last inserted

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Answer: Smallest item

The best option here is Smallest item. Python heapq is a min-heap. That is exactly the concept behind in `heapq`, by default `heappop` returns: in this context. The other options are either incomplete or contextually incorrect.

Q39. `bisect` module is mainly for:

A. Regex matching B. Binary search insertion in sorted lists C. Thread pools D. JSON parsing

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Answer: Binary search insertion in sorted lists

For this question, Binary search insertion in sorted lists is correct. `bisect` finds insertion points quickly in sorted sequences. That is exactly the concept behind `bisect` module is mainly for: in this context. The other options are either incomplete or contextually incorrect.

Q40. What does `functools.partial` create?

A. Generator B. Callable with some arguments pre-filled C. Dataclass D. Decorator class

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Answer: Callable with some arguments pre-filled

Callable with some arguments pre-filled is the correct answer here. Partial application fixes part of function arguments. That is exactly the concept behind what does `functools.partial` create in this context. The other options are either incomplete or contextually incorrect.

Q41. `singledispatch` enables:

A. Method overloading by first argument type B. Async scheduling C. Class inheritance checks D. SQL queries

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Answer: Method overloading by first argument type

Here, Method overloading by first argument type is the right choice. It dispatches to registered implementations based on input type. It fits the requirement in the prompt about `singledispatch` enables:. The other options are either incomplete or contextually incorrect.

Q42. `pathlib.Path` is preferred over raw strings because:

A. It compiles faster B. It provides object-oriented path operations C. It ignores OS differences D. It avoids file permissions

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Answer: It provides object-oriented path operations

In this case, It provides object-oriented path operations is correct. Pathlib offers clearer, cross-platform path manipulation. It fits the requirement in the prompt about `pathlib.path` is preferred over raw strings because:. The other options are either incomplete or contextually incorrect.

Q43. `tempfile.TemporaryDirectory()` ensures:

A. Permanent storage B. Automatic cleanup after context C. GPU acceleration D. Synchronous IO

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Answer: Automatic cleanup after context

The best option here is Automatic cleanup after context. Temporary directory is removed when context exits. It fits the requirement in the prompt about `tempfile.temporarydirectory()` ensures:. The other options are either incomplete or contextually incorrect.

Q44. `subprocess.run(..., check=True)` will:

A. Ignore non-zero exit B. Raise exception on non-zero exit C. Run asynchronously D. Always return stdout

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Answer: Raise exception on non-zero exit

For this question, Raise exception on non-zero exit is correct. It raises `CalledProcessError` if command fails. It fits the requirement in the prompt about `subprocess.run(..., check=true)` will:. The other options are either incomplete or contextually incorrect.

Q45. Difference between `is` and `==` is:

A. No difference B. Identity vs value equality C. String vs number only D. Mutable vs immutable

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Answer: Identity vs value equality

Identity vs value equality is the correct answer here. `is` checks object identity; `==` checks value equivalence. It fits the requirement in the prompt about difference between `is` and `==` is:. The other options are either incomplete or contextually incorrect.

Q46. `copy.copy` performs:

A. Deep copy B. Shallow copy C. No copy D. Thread-safe clone

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Answer: Shallow copy

Here, Shallow copy is the right choice. Top-level object is copied; nested references are shared. This is the most accurate statement for `copy.copy` performs:. The other options are either incomplete or contextually incorrect.

Q47. `copy.deepcopy` is used when:

A. Nested mutable structures need full independent copy B. Object is immutable C. You need faster copy than shallow D. You serialize JSON

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Answer: Nested mutable structures need full independent copy

In this case, Nested mutable structures need full independent copy is correct. Deep copy recursively duplicates contained objects. This is the most accurate statement for `copy.deepcopy` is used when:. The other options are either incomplete or contextually incorrect.

Q48. `__repr__` should ideally be:

A. Human-friendly sentence only B. Unambiguous developer representation C. Always empty string D. Same as `__hash__`

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Answer: Unambiguous developer representation

The best option here is Unambiguous developer representation. `__repr__` is for debugging and should identify object clearly. This is the most accurate statement for `__repr__` should ideally be:. The other options are either incomplete or contextually incorrect.

Q49. `__str__` is intended for:

A. User-friendly display B. Hashing C. Serialization D. Type checking

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Answer: User-friendly display

For this question, User-friendly display is correct. `__str__` produces readable output for end users. This is the most accurate statement for `__str__` is intended for:. The other options are either incomplete or contextually incorrect.

Q50. `__hash__` and `__eq__` should be consistent because:

A. For async support B. Equal objects must hash equal for dict/set correctness C. To speed imports D. To avoid recursion

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Answer: Equal objects must hash equal for dict/set correctness

Equal objects must hash equal for dict/set correctness is the correct answer here. Hash-based collections rely on this contract. This is the most accurate statement for `__hash__` and `__eq__` should be consistent because:. The other options are either incomplete or contextually incorrect.