Advanced Python Test

1. What is the output of the following code?

def func(x, l=[]):
    for i in range(x):
        l.append(i * i)
    return l

print(func(2))
print(func(3, []))
print(func(3))

A) [0, 1] [0, 1, 4] [0, 1, 4]
B) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4]
C) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4]
D) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4, 0, 1, 4]

2. Which statement about metaclasses in Python is TRUE?
A) A metaclass is used to create class instances
B) The __call__ method of a metaclass controls instance creation
C) All classes must explicitly specify a metaclass
D) Metaclasses cannot inherit from other metaclasses

3. What does this decorator do?

from functools import wraps

def debug(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

A) Measures function execution time
B) Logs function calls with arguments
C) Prints the function name when called
D) Prevents function execution in debug mode

4. What is the purpose of context managers?
A) To manage class inheritance hierarchies
B) To handle resource allocation and cleanup
C) To create thread-safe operations
D) To optimize memory usage in loops

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By: t.me/DataScienceQ 🚀

Here are links to the most important free Python courses with a brief denoscription of their value.

1. Coursera: Python for Everybody
Link: https://www.coursera.org/specializations/python
Importance: A perfect starting point for absolute beginners. Covers Python fundamentals and basic data structures, leading to web scraping and database access.

2. freeCodeCamp: Scientific Computing with Python
Link: https://www.freecodecamp.org/learn/scientific-computing-with-python/
Importance: Project-based certification. You build applications like a budget app or a time calculator, reinforcing learning through practical, portfolio-worthy projects.

3. Harvard's CS50P: CS50's Introduction to Programming with Python
Link: https://cs50.harvard.edu/python/2022/
Importance: A rigorous university-level course. Teaches core concepts and problem-solving skills with exceptional depth and clarity, preparing you for complex programming challenges.

4. Real Python Tutorials
Link: https://realpython.com/
Importance: An extensive resource for all levels. Offers in-depth articles, tutorials, and code examples on nearly every Python topic, from basics to advanced specialized libraries.

5. W3Schools Python Tutorial
Link: https://www.w3schools.com/python/
Importance: Excellent for quick reference and interactive learning. Allows you to read a concept and test code directly in the browser, ideal for fast learning and checking syntax.

6. Google's Python Class
Link: https://developers.google.com/edu/python
Importance: A concise, fast-paced course for those with some programming experience. Includes lecture videos and well-designed exercises to quickly get up to speed.

#Python #LearnPython #PythonProgramming #Coding #FreeCourses #PythonForBeginners #Developer #Programming


By: t.me/DataScienceQ 🚀

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❓ Interview question

Why are strings immutable in Python?

Answer: Because once a string is created, its value cannot be changed. This ensures that strings can be safely used in hashing, as dictionary keys, and in multithreaded environments without causing data corruption. Instead of modifying the string, Python creates a new string object when any operation changes it. This behavior improves performance and security but requires more memory for frequent modifications.

tags: #interview #python #strings

By: t.me/DataScienceQ 🚀

❓ Interview question

What is a generator in Python?

Answer: A generator is a special type of function that returns an iterator using the `yield` keyword instead of `return`. It allows you to generate a sequence of values lazily, meaning values are produced on-demand rather than all at once. This saves memory and improves performance when dealing with large datasets.

tags: #python #interview #generator #iterator #programming

By: t.me/DataScienceQ 🚀

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What is the Transformer architecture, and why is it considered a breakthrough in NLP?

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How does self-attention enable Transformers to capture long-range dependencies in text?

❓ Interview question :
What are the main components of a Transformer model?

❓ Interview question :
Why are positional encodings essential in Transformers?

❓ Interview question :
How does multi-head attention improve Transformer performance compared to single-head attention?

❓ Interview question :
What is the purpose of feed-forward networks in the Transformer architecture?

❓ Interview question :
How do residual connections and layer normalization contribute to training stability in Transformers?

❓ Interview question :
What is the difference between encoder and decoder in the Transformer model?

❓ Interview question :
Why can Transformers process sequences in parallel, unlike RNNs?

❓ Interview question :
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❓ Interview question :
What is the role of key, query, and value in attention mechanisms?

❓ Interview question :
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❓ Interview question :
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❓ Interview question :
How does position-wise feed-forward network differ from attention layers in Transformers?

❓ Interview question :
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❓ Interview question :
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❓ Interview question :
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❓ Interview question :
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❓ Interview question :
What is the significance of model size (e.g., number of parameters) in Transformer performance?

❓ Interview question :
How do attention heads in multi-head attention capture different types of relationships in data?

#️⃣ tags: #Transformer #NLP #DeepLearning #SelfAttention #MultiHeadAttention #PositionalEncoding #FeedForwardNetwork #EncoderDecoder

By: t.me/DataScienceQ 🚀

❓ Interview question :
What is NumPy, and why is it essential for scientific computing in Python?

❓ Interview question :
How do arrays in NumPy differ from Python lists?

❓ Interview question :
What is the purpose of ndarray in NumPy?

❓ Interview question :
How can you create a 2D array using NumPy?

❓ Interview question :
What does shape represent in a NumPy array?

❓ Interview question :
How do you perform element-wise operations on NumPy arrays?

❓ Interview question :
What is broadcasting in NumPy, and how does it work?

❓ Interview question :
How do you reshape a NumPy array using reshape()?

❓ Interview question :
What is the difference between copy() and view() in NumPy?

❓ Interview question :
How do you concatenate two NumPy arrays along a specific axis?

❓ Interview question :
What is the role of axis parameter in NumPy functions like sum(), mean(), etc.?

❓ Interview question :
How do you find the maximum and minimum values in a NumPy array?

❓ Interview question :
What are ufuncs in NumPy, and give an example?

❓ Interview question :
How do you sort a NumPy array using np.sort()?

❓ Interview question :
What is the use of np.where() in conditional indexing?

❓ Interview question :
How do you generate random numbers using NumPy?

❓ Interview question :
What is the difference between np.random.rand() and np.random.randn()?

❓ Interview question :
How do you load data from a file into a NumPy array?

❓ Interview question :
What is vectorization in NumPy, and why is it important?

❓ Interview question :
How do you calculate the dot product of two arrays in NumPy?

#️⃣ tags: #NumPy #Python #ScientificComputing #Array #ndarray #ElementWiseOperations #Broadcasting #Reshape #CopyView #Concatenation #AxisParameter #MaximumMinimum #ufuncs #Sorting #ConditionalIndexing #RandomNumbers #DataLoading #Vectorization #DotProduct

By: t.me/DataScienceQ 🚀