Intro to Computational thinking

What is an algorithm in the context of computing?

• An algorithm is a type of hardware in computing

• An algorithm is a programming language in computing

• An algorithm in computing is a set of well-defined instructions for solving a problem.

• An algorithm is a storage device in computing

Why is abstraction important in solving computational problems?

• Abstraction simplifies the complexity of a problem and allows for the design of efficient solutions.

• Abstraction complicates problem-solving and makes solutions inefficient.

• Abstraction has no impact on the complexity of a computational problem.

• Abstraction is only relevant in simple problems, not in complex ones.

Explain the concept of decomposition in problem-solving.

• Decomposition breaks complex problems into simpler parts for easier solving.

• Decomposition refers to a chemical process

• Decomposition is exclusive to biology

• Decomposition involves joining elements to solve problems

Why is pattern recognition crucial in computing?

• To generate confusion in the results

• To increase the complexity of the data without any benefit

• To complicate the decision-making process

• It is crucial to identify regularities in complex data and facilitate automated decision-making.

Describe the problem-solving process in computational thinking.

• Trying random solutions until one works

• Not breaking the problem down into smaller parts

• Guessing the solution without analyzing the problem

• Breaking the problem into smaller parts, identifying patterns, designing algorithms, implementing and testing those algorithms, and combining the solutions.

What does modeling a problem mean in the context of computing?

• Ignore the problem structure completely

• Copy the solution from another similar problem

• Solve a problem without using computer tools

• Represent a problem using tools such as diagrams, pseudocode, or mathematical models.

Explain the importance of transformation and composition in programming.

• They are irrelevant and do not provide benefits

• They only serve to complicate the code

• They are essential for code reuse, modularization, and creating readable and maintainable programs.

• They are just theoretical concepts without practical application

What are the implications of encapsulation and generalization in algorithm design?

• Increase in complexity, reduction in readability, loss of modularity

• Simplification, code reuse, modularity, and flexibility.

• Limitation in code reuse, increase in errors, rigidity in design

• Greater flexibility, less modularity, difficulty in simplifying algorithms

Why are data structures important in programming?

• Data structures are important for organizing and storing data efficiently, facilitating the manipulation, search, and retrieval of information optimally.

• Data structures only serve to complicate programs

• Data structures do not affect the performance of a program

• Data structures are irrelevant in programming

How can data structures improve algorithm efficiency?

• By using data structures such as strings, random numbers, or images.

• By using data structures like text files, databases, or web servers.

• By using data structures like arrays, linked lists, trees, graphs, to store and manipulate data efficiently.

• By applying data structures like stacks, queues, or sets, to improve efficiency.