Python Testing and Debugging

Python Testing and Debugging - Important Points

21.		Which module in Python provides support for unit testing?

A. unittest

B. pytest

C. doctest

D. all of the above

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Answer: option d

Explanation:

Python provides support for unit testing through multiple modules, including unittest, pytest, and doctest. Each module has its own features and advantages, and developers can choose the module that best fits their needs.

22.		What is a mock object in Python?

A. An object that simulates the behavior of a real object in a controlled way

B. An object that is used to bypass security measures in a program

C. An object that is used to test network connections

D. An object that is used to generate random data for testing

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Answer: option a

Explanation:

A mock object is an object that simulates the behavior of a real object in a controlled way. Mock objects are often used in testing to replace real objects that are difficult to work with, such as objects that depend on external resources or have complex behavior.

23.		What is code coverage in Python testing?

A. The percentage of code that is executed during testing

B. The amount of time it takes to run a test suite

C. The number of test cases that are run during testing

D. The number of bugs that are found during testing

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Answer: option a

Explanation:

Code coverage is a measure of the percentage of code that is executed during testing. It is used to identify code that is not being tested and may contain bugs or other issues.

24.		What is a breakpoint in Python debugging?

A. A point in the code where execution is paused to allow for inspection

B. A point in the code where execution is terminated

C. A point in the code where a specific action is triggered

D. A point in the code where an error is expected to occur

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Answer: option a

Explanation:

A breakpoint is a point in the code where execution is paused to allow for inspection. Developers can use breakpoints to step through the code and examine the state of the program at specific points in the execution.

25.		Which tool in Python can be used for profiling and optimizing code performance?

A. cProfile

B. timeit

C. memory_profiler

D. all of the above

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Answer: option d

Explanation:

Python provides multiple tools for profiling and optimizing code performance, including cProfile, timeit, and memory_profiler. Each tool provides different insights into the performance of the code and can be used to identify areas for improvement.

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