How would you implement a stack in Python?
- Using a dictionary
- Using a list
- Using a set
- Using a tuple
In Python, you can implement a stack using a list. Lists provide built-in methods like append() and pop() that make it easy to simulate a stack's behavior.
How would you implement rate limiting in a RESTful API to prevent abuse?
- A. Use JWT tokens
- B. Implement a token bucket algorithm
- C. Limit requests based on IP addresses
- D. Disable API access during peak hours
B. Implementing a token bucket algorithm is a common method for rate limiting in RESTful APIs. It allows you to control the rate of requests over time, preventing abuse while allowing legitimate usage. Options A, C, and D are not effective methods for rate limiting.
How would you initialize an empty list in Python?
- empty_list = []
- empty_list = [None]
- empty_list = {}
- empty_list = None
To initialize an empty list in Python, you use square brackets []. Option 2 initializes an empty dictionary, option 3 initializes a variable as None, and option 4 initializes a list with a single element, which is None.
How would you investigate memory leaks in a Python application?
- Manually inspect each variable in the code to find memory leaks.
- Use a memory profiler like memory_profiler to track memory usage over time.
- Use the psutil library to monitor CPU usage and infer memory leaks.
- Use the timeit module to measure execution time and find memory leaks.
To investigate memory leaks in a Python application, you can use a memory profiler like memory_profiler, which tracks memory usage over time, helping you identify areas of concern. Manual inspection (Option 3) is impractical for large codebases, and psutil (Option 2) primarily focuses on CPU usage. The timeit module (Option 4) measures execution time, not memory usage.
How would you optimize a Python function that is found to be CPU-bound during profiling?
- a) Use a Just-In-Time (JIT) compiler like PyPy.
- b) Increase the number of threads to parallelize the code.
- c) Optimize the algorithm or use data structures that are more efficient.
- d) Use a faster computer for running the code.
When a Python function is CPU-bound, the most effective optimization is usually to optimize the algorithm or use more efficient data structures. JIT compilation (a) can help in some cases, but it may not be as effective as algorithmic improvements. Increasing the number of threads (b) might help if the code can be parallelized, but this is not always the case. Using a faster computer (d) is generally not a solution to CPU-bound code as it doesn't address the underlying inefficiencies.
In Pandas, how do you access the first five rows of a DataFrame?
- df.head()
- df.iloc[:5]
- df.loc[:5]
- df[0:5]
To access the first five rows of a Pandas DataFrame, you should use the head() method, like df.head(). This method returns the top N rows (default is 5) of the DataFrame.
In pytest, the ____ fixture is used to execute specific finalization code after the test function has completed.
- cleanup
- finalize
- teardown
- yield_fixture
In pytest, the teardown fixture is used to execute finalization code after the test function has completed its execution. This allows you to perform cleanup tasks or teardown actions after the test has run, such as closing a database connection or cleaning up temporary files.
In pytest, the ____ fixture is used to pass command-line options to test functions.
- @pytest.cmdline
- @pytest.config
- @pytest.fixture(params)
- @pytest.options
In pytest, the @pytest.config fixture is used to pass command-line options and configuration values to test functions. This allows you to customize the behavior of your tests based on configuration settings.
In pytest, the ____ marker is used to skip a test function under certain conditions.
- @pytest.ignore
- @pytest.run
- @pytest.skip
- @pytest.xfail
In pytest, the @pytest.skip marker is used to skip a test function when certain conditions are met. This allows you to selectively skip tests based on runtime conditions or configurations.
In Python, ____ is used to access the attributes and methods of a class.
- Class
- Dot notation
- Inheritance
- Object
In Python, you use the dot notation (.) to access the attributes and methods of a class. For example, object.attribute or object.method().