PLTW Computer Science Principles Quiz

What are the four main data types in programming?

• character, double, boolean, array

• integer, double, string, boolean

• integer, float, character, boolean

• integer, float, string, boolean

What is an algorithm?

• A mathematical equation used to solve complex problems.

• A step-by-step procedure or a set of rules for solving a specific problem or accomplishing a specific task.

• A random guess or estimate.

• A type of computer program.

Name three popular programming languages.

• C++, Ruby, HTML

• Python, Java, JavaScript

What is computational thinking?

• A process of solving problems by randomly guessing and checking different solutions.

• A method of thinking that involves using emotions and intuition to solve problems.

• A problem-solving process that involves breaking down complex problems into smaller, more manageable parts and using algorithms and logical reasoning to solve them.

• A way of thinking that focuses on memorizing and regurgitating information.

What is data analysis?

• The process of visualizing data

• The process of collecting and storing data

• The process of inspecting, cleaning, transforming, and modeling data to discover useful information.

• The process of creating data

Which data type is used to store whole numbers in programming?

• string

• integer

• float

• boolean

What is the process of solving a problem step by step called?

• algorithm

• randomization

• iteration

• heuristic

Which programming language is commonly used for web development?

• JavaScript

• Ruby

• C++

• Python

What are the key components of computational thinking?

• decomposition, pattern recognition, abstraction, and algorithm design

• visualization, collaboration, creativity, and critical thinking

• coding, debugging, testing, and optimization

• iteration, problem solving, logical reasoning, and data analysis

What techniques are used in data analysis?

• Data storage, data retrieval, data encryption

• Data sorting, data filtering, data aggregation

• Data cleaning, data transformation, data visualization, statistical analysis, machine learning, and data mining.

• Data modeling, data validation, data sampling