# Problem Solving Using Computational Thinking Quiz Answers

## Get All Weeks Problem Solving Using Computational Thinking Quiz Answers

### Problem Solving Using Computational Thinking Week 01 Quiz Answers

#### Foundations of Computational Thinking Practice Questions Answers

Q1. In your own words, define the following aspects of computational thinking: Problem Identification, Decomposition, Pattern Recognition, and Abstraction.

• Problem Identification: This is the initial step in computational thinking where you recognize and define a specific issue or challenge that needs to be addressed using computational methods. It involves clearly understanding what the problem is and what the desired outcome or solution should be.
• Decomposition: Decomposition is the process of breaking down a complex problem into smaller, more manageable sub-problems or tasks. It simplifies the problem-solving process by dividing it into smaller components, making it easier to understand and tackle.
• Pattern Recognition: Pattern recognition is the ability to identify common patterns or trends within data or problems. It involves recognizing similarities or recurring themes that can help in finding solutions more efficiently.
• Abstraction: Abstraction is the process of simplifying complex systems or ideas by focusing only on the essential details and ignoring irrelevant information. It allows us to create models and representations that capture the key aspects of a problem while eliminating unnecessary complexities.

Q2. Once you’ve identified a problem that may be ‘solvable’ through computational thinking,  you need to decompose the topic further by creating sub-problems. In your own words, explain why we need to decompose complex problems in order for a computer to solve it.

• Decomposing complex problems is essential for computers to solve them because computers work in a step-by-step, algorithmic manner. By breaking down a complex problem into smaller sub-problems, we make it easier for a computer to process and solve each sub-problem sequentially. This approach simplifies the task for the computer, as it can handle smaller, well-defined tasks more efficiently. Additionally, decomposing a problem makes it more understandable for both humans and machines, as it reduces the cognitive load associated with tackling the entire problem at once. In summary, decomposition helps in organizing and structuring the problem-solving process, making it feasible for computers to work through complex issues systematically.

#### Foundations of Computational Thinking Quiz Answers

Q1. In computational thinking terms, breaking down a complex problem into smaller, more specific sub-problems is called ___________.

• Decomposition
• Pattern Recognition
• Problem Identification

Q2. True or False: Computational thinking techniques can help programmers conceptualize problems before they begin programming.

• False
• True

Q3. In computational thinking terms, framing a problem and determining if it can be solved by computers is known as _________.

• Abstraction
• Problem Identification
• Pattern Recognition

Q4. While writing a program for building a cake, you decide that some information is less relevant for your particular program. For instance, you might decide that you don’t need to know the flavor of ice cream that the cake is being served with, and you don’t need to know what color plates the cake is being served on. In computational thinking terms, this process of ignoring or filtering out less relevant information is known as _______.

• Decomposition
• Abstraction
• Pattern Recognition

Q5. True or False: When identifying a problem for a computer to solve, it is best to identify problems that are subjective or open-ended.

• True
• False

Q6. True or False: Computational thinking is a linear process.

• False
• True

#### Airport Surveillance Practice Questions Set 1 Answers

Q1. As discussed in the Airport Surveillance case study, computer-based solutions require questions that are specific and quantifiable. Based on these criteria, which of the following questions is most appropriate for a computer-based solution?

• How can I monitor suspicious behavior?
• How do I distinguish between luggage that is moving and luggage that is stationary?
• How can I identify potentially dangerous luggage?
• All of the above could be solved by a computer.

Q2. True or False: Asking a computer to monitor “suspicious behavior” is a specific and quantifiable problem that is suitable for a computer-based solution.

• False
• True

Q3. Decomposition consists of various layers of breaking down a problem. Consider managing security at a crowded public event. How would you decompose the task of identifying suspicious behavior at a concert? In other words, which of the following are possible sub-pieces of this problem? (Select all that apply).

• Identify possible weapons carried by concert-goers.
• Identify what song is being played at the concert.
• Identify suspicious packages left unattended and idle.
• Identify what color shirts people are wearing at the concert.
• Identify concert-goers who are consistently not paying attention to the musician.

Q4. Consider how you would further break down the task of “identifying possible weapons carried by concert-goers.” To help with this problem, you have at your disposal surveillance video from multiple angles, handheld metal detectors, and bag X-ray scanners at the venue entrances. Which of the following are possible pieces of information you will likely need to carry out the task? (Select all that apply).

• The shapes of different kinds of weapons.
• Common ways of holding handheld weapons.
• The value (price) of different kinds of weapons.
• What do different kinds of weapons sound like?
• What material weapons are often made of?

#### Airport Surveillance Practice Questions Set 2 Answers

Q1. You are currently developing a new surveillance algorithm that flags illegal substances for airport security officers. You are still early in the development process, and there are some known issues with the algorithm. Given these criteria, which of the following situations would be most appropriate for testing this new algorithm?

• A large, crowded airport, with 24 hours of video that needs to be analyzed.
• A small, unoccupied airport, with 24 hours of video needs to be analyzed.
• A small, unoccupied airport, with a 10-minute long video that needs to be analyzed.
• A crowded airport with 10 minutes of video that needs to be analyzed.

Q2. True or False: Machine Learning is a subset of Artificial Intelligence.

• False
• T​rue

Q3. In the airport surveillance case study, what kinds of technology are needed in order to identify objects and people? Please select the best answer.

• Artificial Intelligence
• Machine Learning
• Algorithms
• All of the above

Q4. To solve the problem of finding suspicious packages/luggage in an airport, which elements in the video can be ignored from video analysis?

• Background signs and posters
• The proximity between people and luggage
• People who are not moving
• Where packages/luggage are in the frame

Q5. In your own words, describe (1) what an algorithm is, and (2) what an algorithm is used for.

• Decomposition in computational thinking involves breaking down a complex problem or task into smaller, more manageable sub-problems or components. It’s like dividing a large puzzle into smaller pieces that are easier to solve individually. By decomposing a problem, it becomes easier to understand, tackle, and address each component separately. This approach simplifies problem-solving and allows for a more organized and efficient strategy, especially when dealing with intricate or multifaceted challenges.

#### Airport Surveillance Case-Study Quiz Answers

Q1 .Identifying suspicious behavior at an airport is a complex problem. In this case study, what was one strategy for decomposing this problem into a smaller, more manageable problem?

• Use machine learning to track which parts of the airport is the busiest.
• Define a specific type of suspicious behavior in quantifiable terms.
• Design an algorithm that counts how often luggage is left unattended.
• Design an algorithm that can differentiate between airport staff and travelers.

Q2. When designing an algorithm that will detect unattended luggage, what kind of information would likely NOT be ​relevant to this problem?

• Whether a piece of luggage is idle or moving.
• The types of clothing people in an airport are wearing.
• The length of time luggage has been left unattended.
• The distance between attended luggage and its owner.

Q3. Using the following algorithm, what would happen if the luggage in one video frame is not seen in the next frame?

• The algorithm generates a warning.
• No warning is given, and the algorithm checks if there are more objects to categorize in the frame.
•  The algorithm checks to see how long the luggage has been moving.
• The algorithm checks to see if the luggage is accompanied by a human.

Q4. Since computer-based solutions require questions that are specific and quantifiable, which one of the following questions is most appropriate for a computer-based solution?

• What kind of luggage is the most aesthetically pleasing?
• How many people have entered the airport in the past two hours?
• Why is flying better than driving a car?
• What kind of behavior is suspicious?

Q5. What is an algorithm? Choose the best answer:

• The breaking down of a large, complex problem, into smaller more manageable problems.
• The process of identifying patterns that can lead you to a potential solution.
• The process of identifying parts of a problem that can be ignored when approaching a problem.
• A process or defined set of rules used by a computer for solving an identified problem.

### Week 03: Problem Solving Using Computational Thinking Quiz Answers

Q1. In the epidemiology case study, we constructed the following model:

In this model, S represents the number of people susceptible to infection, b represents the rate of infection, I represents the number of people infected, r represents the recovery rate, and R represents the number of people who have recovered from infection.

Using this algorithm, what changes would we expect if people stopped washing their hands and covering their coughs during flu season?

• The rate of infection (b) would increase, which would result in more infected people (I) and ultimately more recovered people (R).
• The rate of infection (b) would decrease, which would result in fewer infected people (I).
• The recovery rate (r) would decrease, resulting in more recovered people (R).

Q2. In the epidemiology case study, what was one-way decomposition was used to identify a sub-problem?

• One way decomposition was used in the epidemiology case study to identify a sub-problem was by breaking down the overall problem of disease spread into specific components, such as the number of susceptible individuals (S), the rate of infection (b), the number of infected individuals (I), the recovery rate (r), and the number of recovered individuals (R). Each of these components represents a sub-problem that can be analyzed and modeled separately, contributing to a comprehensive understanding of disease dynamics.

Q3. In the epidemiology case study, we expanded on the SIR model by adding information about vaccinations. The expanded model looked like this:

In this expanded model, the number of vaccinations (V) decreases the number of people who are susceptible to infection (S).

Using this model, what would we expect to happen if we increased the number of vaccinated people (V)?

• The number of recovered people (R) will increase.
• The infection rate (b) will increase.
• The number of infected people (I) will decrease.

Q4. When testing a new algorithm, it’s usually best to run your algorithm on a simplified test case. For instance, in the airport surveillance case study, testing a new algorithm meant scanning video with lower resolution, or scanning shorter video clips.

Describe how you might test a new epidemiology algorithm that predicted the number of people prone to becoming infected with the flu.

• This statement is true. When testing a new algorithm, it is often best to start with simplified test cases or scenarios to assess its functionality and performance. This allows for initial validation and debugging before applying the algorithm to more complex and resource-intensive situations. Lower-resolution video or shorter video clips can provide a controlled environment for testing and refinement before scaling up to real-world scenarios.

Q1. In the epidemiology case study, we constructed the following algorithm:

In this algorithm, S represents the number of people susceptible to infection, b represents the rate of infection, I represents the number of people infected, r represents the recovery rate, and R represents the number of people who have recovered from infection.

Using this algorithm, what changes would we expect if more people washed their hands and covered their coughs during flu season?

• The number of susceptible people (S) would increase, which would result in an increased number of infected people (I).
•  The rate of infection (b) would decrease, which would result in fewer infected people (I).
• The number of infected people (I) would increase, which would result in more recovered people (R).
• The recovery rate (r) would decrease, resulting in more recovered people (R).

Q2. In the epidemiology case study, the SIR model accounted for the number of people susceptible to infection, the rate of infection, the number of people infected, the rate of recovery, and the number of people who recovered from the infection. If we wanted to create a more accurate model for predicting the spread of the flu, what information would be most relevant for this problem?

• The migration patterns of infected people.
• The amount of electricity people use in their homes.
• The number of cell phone calls people make in a day.
• The dental records of susceptible people in a given location.

Q3. Predicting the number of people who will become infected with the seasonal flu can be a complex problem. In computational thinking terms, describing this complex problem in such a way that it can be solved by a computer is known as __________.

• Abstraction
• Evaluation
• Problem Identification
• Pattern Recognition

Q4. In the epidemiology case study, the SIR model utilized the following information: the number of people susceptible to infection (S), the rate of infection (b), the number of people infected (I), the recovery rate (r), and the number of people who recovered from infection (R). This process of focusing on relevant information and ignoring less relevant information represents what computational thinking technique?

• Decomposition
• Abstraction
• Evaluation
• Problem Identification

Q5. In the epidemiology case study, we expanded on the original SIR model by adding information about vaccinations. The expanded model looked like this:

In this expanded model, the number of vaccinations (V) decreases the number of people who are susceptible to infection (S).

Using this algorithm, what will happen to the number of people recovered (R) at the end of an epidemic if we increase V at the beginning?

• The number of people recovered (R) will decrease.
• The number of people recovered (R) will stay the same.
• The number of people recovered (R) will increase.

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