Computational Thinking Practices Flashcards

Which of the following is an example of abstraction in computer science?

Answer: Using a function to perform a complex task without worrying about the implementation details

Abstraction in computer science involves hiding complex implementation details and showing only the essential features. Using a function is a prime example: you call the function by its name, provide inputs, and get an output, without needing to know the intricate steps or code inside the function that produce that output.

What is the purpose of an algorithm in computer science?

Answer: To provide a step-by-step solution to a problem

An algorithm is a precise, unambiguous sequence of instructions or rules designed to solve a specific problem or perform a computation. It outlines the exact step-by-step procedure needed to achieve a desired outcome, forming the logical foundation for computer programs.

Which of the following best describes "decomposition" in computational thinking?

Answer: Breaking a problem into smaller, more manageable parts

Decomposition is a fundamental concept in computational thinking where a complex problem is broken down into smaller, simpler sub-problems. This makes the overall problem easier to understand, analyze, and solve, as each sub-problem can be tackled individually and systematically.

A software developer is building a new app that requires the ability to perform calculations. Which of the following is the most effective way to design the app’s calculation feature?

Answer: Use an already-defined function to handle the calculations

Using an already-defined function promotes efficiency, reusability, and abstraction in software development. It avoids redundant code, reduces the chance of errors, and allows the developer to focus on higher-level logic rather than re-implementing basic functionalities like calculations from scratch.

Which of the following is an example of pattern recognition in computational thinking?

Answer: Identifying a recurring sequence of numbers in data

Pattern recognition in computational thinking involves identifying similarities, trends, or recurring themes within data. Recognizing a recurring sequence of numbers directly exemplifies this by finding predictable structures or regularities in a dataset. This process is crucial for simplifying problems, making predictions, and developing efficient algorithms.