Every AP CSP exam question, multiple-choice or Create performance task, is tied to one of six computational thinking practices. This collection gives you a focused guide for each practice so you can identify exactly where your skills are strong and where to spend more time.
Use the six practice guides to review how each skill is tested, then use the score calculator to estimate where you stand heading into the exam.
AP CSP organizes everything students do around six computational thinking practices. These are not just categories on a syllabus. Every multiple-choice question and every Create written response is scored against one or more of these practices. Knowing which practice a question targets helps you answer it more precisely.
Practices 1, 2, 3, and 4 all connect to the Create performance task. When you write your written responses, you are demonstrating that you can design a solution, represent and implement an algorithm, use abstraction, and explain how your code functions. Reviewing these four practices before you finalize your Create responses is time well spent.
Practice 5 (Computing Innovations) is tested exclusively on the multiple-choice section. Questions ask you to explain how systems work, describe how data becomes knowledge, identify impacts of an innovation, and evaluate legal and ethical considerations. No Create written response targets Practice 5 subskills directly.
Responsible Computing (Practice 6) covers collaboration, safe computing habits, and crediting others' work. It is not scored through a standalone multiple-choice question set or a separate written response. It shows up in how you conduct your Create work and in scattered multiple-choice contexts. The guide explains what this means for your exam strategy.
AP CSP questions are written to test what you can do, not just what you know. A question about a sorting algorithm is really testing whether you can trace code (Practice 4) or represent the algorithm without code (Practice 2). Recognizing the practice behind a question tells you what kind of thinking the question wants, which makes it easier to eliminate wrong answers and construct strong written responses.
This guide covers how to investigate a task, design a method that fits the purpose, evaluate competing options, and use collaboration effectively. It is especially useful before you write your Create task responses about why you made the design choices you did.
This guide covers representing algorithms in plain language or diagrams (2.A) and implementing or tracing algorithms in code (2.B). Multiple-choice questions frequently hand you pseudocode and ask what it produces or how to fix it.
This guide covers using variables to stand in for data, building procedures that hide complexity, calling those procedures, and explaining why abstraction makes programs easier to write and change. The Create task scores you on whether your program uses meaningful abstraction.
This guide covers the three subskills: explaining what a code segment does, tracing through code to find the output, and identifying and correcting errors. Code analysis questions are among the most common on the multiple-choice section.
This guide covers how computing systems function, how data is collected and turned into knowledge, what beneficial and harmful impacts an innovation creates, and how legal and ethical considerations shape computing. This practice is multiple-choice only, so the guide focuses on reading and reasoning strategies.
This guide covers inclusive collaboration, safe and secure computing habits, and properly crediting the work of others. Because Practice 6 is not scored through a standalone question type, the guide explains where it does appear and how to handle it in your Create work.
Each of the six guides in this collection covers one practice in depth, including its subskills, how it appears on the exam, and worked examples. If you are preparing for the multiple-choice section, prioritize Practices 2, 4, and 5 because they generate the highest volume of question types. If you are finalizing your Create task, focus on Practices 1, 2, 3, and 4 in that order.
| Practice | Multiple-choice | Create task |
|---|---|---|
| 1: Computational Solution Design | Yes | Yes |
| 2: Algorithms and Program Development | Yes | Yes |
| 3: Abstraction in Program Development | Yes | Yes |
| 4: Code Analysis | Yes | Yes |
| 5: Computing Innovations | Yes | No |
Practice 3 is about more than naming a variable. The exam also asks you to explain why abstraction is useful, how a procedure hides complexity, and how calling a procedure with different parameters generalizes a solution. Students who only think about variables miss the procedural abstraction questions.
Practice 5 questions have specific vocabulary around data collection, storage, processing, and impacts. Terms like metadata, personally identifiable information, and digital divide appear in precise ways. Reviewing the Practice 5 guide helps you answer these questions accurately rather than relying on general intuition.
While Practice 6 is not scored through a standalone question type, collaboration and crediting sources do appear in multiple-choice scenarios and in the Create task context. Ignoring the guide entirely can cost points on questions about open-source licensing, Creative Commons, and collaborative development.
Practice 2 has two distinct subskills. Skill 2.A is about representing an algorithm without a programming language, using plain language or a diagram. Skill 2.B is about implementing and applying an algorithm in code. Exam questions test these separately, and mixing them up leads to answers that address the wrong skill.
Practice 4 subskill 4.A asks you to explain how a code segment functions, not just restate what the lines say. A response that says 'the loop runs ten times' without explaining what changes each iteration or what the result means will not earn full credit.
When you see a multiple-choice question with a code segment, the question is testing Practice 2, 3, or 4 depending on what it asks. Recognizing the practice helps you focus: a question asking what a procedure returns is a Practice 4 trace task, while a question asking how to rewrite code to reduce repetition is a Practice 3 abstraction task.
The Create task prompts are designed to elicit evidence of Practices 1 through 4. The response about your algorithm targets Practice 2. The response about your procedure and what it does targets Practices 3 and 4. Knowing which practice each prompt targets helps you write responses that include the right kind of evidence rather than general descriptions.
A typical Practice 5 multiple-choice question introduces a technology, describes how it collects or uses data, and then asks about impacts, privacy concerns, or ethical considerations. The Practice 5 guide prepares you to read these scenarios efficiently and match the details to the correct category of impact or the correct legal concept.
Open the individual guides for Computational Thinking Practices when you want a closer review of one topic.
browse guidesPractice free-response reasoning and compare your answer with scoring guidance.
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