HOMEWORK 1 Computational thinking

Q1. What is Decomposition?

• Breaking down a complex problem into smaller, simpler parts which are easier to solve
• Not breaking down a complex problem into smaller, simpler parts which are easier to solve
• Build a easier problem into larger, simpler parts which are easier to solve
• Help a company to build a complex problem into bigger section

Q2. What is Pattern recognition ?

• Looking for similarities within a problem
• Not looking for similarities within a problem
• Hiring someone to not look for similarities within a problem
• Looking for differences within a problem

Q3. What is Abstraction?

• Focussing only on important information, ignoring irrelevant detail
• Not focussing on important information, ignoring irrelevant detail
• Focussing only on less information, but using irrelevant detail
• Using irrelevant detail

Q4. What is an Algorithm?

• A step-by-step solution to a problem, it must be simple, easy to follow, and precise.
• Not a step-by-step solution to a problem, it must not be simple, not easy to follow.
• Not a solution to a problem.
• A bigger problem.

Q5. One advantage of abstraction is that it means no mistakes can be made.

• True
• False

Q6. You could use abstraction to replace specific objects with simple shapes.

• True
• False

Q7. Abstraction and Decomposition are examples of...

• Computational Thinking
• Computer Science
• Algorithmic Thinking
• Logical Reasoning

Q8. Why do we need to look for patterns in problems?

• Patterns make it easier for us to solve complex problems.
• We don't need to look for patterns.
• Patterns make it more difficult complex problems.
• Don't bother with patterns.

Q9. Which of the following is an example of thinking computationally?

• Planning out your route when going to meet a friend
• When going to meet a friend, wandering around until you find them
• When going to meet a friend, asking a parent to plan your route for you

Q10. Looking for similarities and trends within a problem.

• Abstraction
• Decomposition
• Pattern recognition
• Algorithmic Thinking