06.07 Code Optimisation And Assessing Effectiveness - Quiz

Check your understanding

1. Which four dimensions are used to assess code effectiveness?

   • Speed, memory, disk space, network usage
   • Correctness, clarity, performance, maintainability { data-correct }
   • Syntax, logic, structure, documentation
   • Input, processing, output, storage

2. What is the principle of “avoiding premature optimization”?

   • Never optimize code under any circumstances
   • Optimize everything from the beginning to save time later
   • Don’t optimize until you know where the bottlenecks actually are { data-correct }
   • Only optimize code that looks complex

3. Which data structure choice would provide the best performance for frequent membership testing (checking if an item exists)?

   • List
   • Set { data-correct }
   • Tuple
   • String

4. What is the most effective type of optimization for improving algorithm performance?

   • Using shorter variable names
   • Removing comments and whitespace
   • Choosing algorithms with better time complexity { data-correct }
   • Using more advanced language features

5. When should you prioritize code readability over performance optimization?

   • Never - performance is always most important
   • When the performance gain is minimal and the code is not a bottleneck { data-correct }
   • Only in prototype code
   • When using interpreted languages

6. What is the primary purpose of profiling code?

   • To check for syntax errors
   • To identify actual performance bottlenecks before optimizing { data-correct }
   • To count the number of lines of code
   • To verify code correctness

7. Which of the following demonstrates better maintainability?

   • All code in one large method to avoid method call overhead
   • Code separated into focused methods with clear responsibilities { data-correct }
   • Using single-letter variable names to save typing
   • Avoiding comments to keep code concise

8. In the context of Big O notation, which represents the best time complexity for a search algorithm?

   • O(n²)
   • O(n log n)
   • O(n)
   • O(1) { data-correct }

9. What should you do first when you suspect a performance problem in your code?

   • Immediately start rewriting the slowest-looking parts
   • Profile the code to measure where time is actually spent { data-correct }
   • Switch to a faster programming language
   • Add more comments to understand the code better

10. Which approach best balances all four dimensions of code effectiveness?

    • Write the fastest possible code regardless of readability
    • Focus only on making code work correctly, ignore other factors
    • Write clear, correct code first, then optimize identified bottlenecks { data-correct }
    • Optimize everything from the start to avoid later rewrites