Practice Execution Time And Performance (15.5) - Efficiency - Data Structures and Algorithms in Python
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Execution Time and Performance

Practice - Execution Time and Performance

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary focus when measuring algorithms' efficiency?

💡 Hint: Think about what performance metric is critical for algorithm selection.

Question 2 Easy

What do we denote worst-case time complexity as?

💡 Hint: Consider the notation commonly used in assessing complexity.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Big O notation describe?

Exact time complexity
Upper bounds of time complexity
Average-case performance

💡 Hint: Think about the focus of the notation.

Question 2

True or False: Linear search has a better time complexity than binary search.

True
False

💡 Hint: Consider the input arrangement required by each search method.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given an algorithm with a time complexity of O(n^3), how many operations can you reasonably expect to perform on an input of size 150?

💡 Hint: Calculate n^3 to understand how large the operation count becomes.

Challenge 2 Hard

If a certain algorithm runs for 2^n time complexity, for what maximum 'n' can it run within 10 seconds on a reasonable computer? Discuss.

💡 Hint: Think about how quickly the count increases as 'n' grows!

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