AP Computer Science Principles, AP exams, 17–22%

Big Idea 2: Data

Big Idea 2 covers how computers represent information and how information is extracted from it. Binary representation, the difference between lossy and lossless compression, and the process of cleaning and filtering data to find patterns are the core topics. The unit connects to the impact big idea whenever data quality or bias comes up.

Position: 2 of 5 units
Published weighting: 17–22% in the official framework
Topics covered: 4 topics in this guide

What is covered

Topics inside this unit.

These topics describe the scope students most often review for this part of AP Computer Science Principles. Treat them as a study checklist rather than an exhaustive list of what a single exam form can sample.

Binary numbers: Representing numbers in binary, converting between binary and decimal, and the limits of fixed-size storage.
Data compression: Lossless versus lossy compression, and the trade-off between file size and fidelity.
Extracting information from data: Cleaning, filtering, and transforming data, and the difference between correlation and causation in findings.
Using programs with data: How programs process large data sets, and the role of metadata in searching and organizing information.

Study tips

How to practice Data.

Section-specific guidance that follows from how this material is usually tested.

Practice binary to decimal conversion in both directions until it is fast; it appears reliably on the exam.
Keep one concrete example each of lossy and lossless compression and be able to say when each is preferred.
Rehearse explaining what cleaning a data set involves and why it changes the conclusions you can draw.

Where this fits

Big Idea 2: Data is 2 of 5 units in the AP Computer Science Principles guide, and it carries a published weighting of 17–22% in the official framework. Use the full guide to balance it against everything else you have to review.

Back to the AP Computer Science Principles guide Official exam information

Sample Data questions

Practice questions, not yet expert-reviewed

Original practice questions written to the published framework for this section. No expert has reviewed them, and they are not taken from any official exam. Try each one before you open the answer.

Question 1Foundational
What is the decimal value of the binary number 1011?
1. A11
2. B13
3. C7
4. D23
Show answer
Answer: A. 11
The place values from left to right are 8, 4, 2, and 1, so 1011 represents 8 + 0 + 2 + 1 = 11. Reading the digits in the wrong direction gives 13, which is the value of 1101.
Key insight: Binary place values double from right to left: 1, 2, 4, 8, 16, and so on.
Question 2Foundational
A file is compressed in a way that allows the original file to be reconstructed exactly. What kind of compression is that?
1. ALossless compression
2. BLossy compression
3. CEncryption
4. DMetadata extraction
Show answer
Answer: A. Lossless compression
Lossless compression removes redundancy only, so decompression restores the original bits exactly. Lossy compression discards information permanently to reach smaller sizes, and encryption changes who can read the data rather than how much space it takes.
Key insight: Lossless keeps every bit recoverable; lossy trades fidelity for a smaller file.
Question 3Intermediate
How many different values can be represented using 6 bits?
1. A64
2. B32
3. C36
4. D12
Show answer
Answer: A. 64
Each bit doubles the number of possible combinations, so 6 bits represent 2^6 = 64 distinct values. Answering 32 uses 5 bits, and 36 squares the number of bits instead of raising 2 to that power.
Key insight: n bits represent 2^n values, which is why one more bit doubles the range.
Question 4Intermediate
A survey data set contains several spellings of the same city name and a number of blank entries. What process prepares this data set for analysis?
1. ACleaning the data
2. BCompressing the data
3. CEncrypting the data
4. DAdding metadata to the data
Show answer
Answer: A. Cleaning the data
Cleaning standardizes inconsistent entries and decides how to handle missing values so that later counts and comparisons are meaningful. Compression changes storage size, encryption changes access, and metadata describes the data set without correcting the entries themselves.
Key insight: Conclusions drawn from uncleaned data can be wrong even when the analysis code is correct.
Question 5Advanced
Analysis of a large data set shows that ice cream sales and swimming pool visits rise and fall together across the year. What is the strongest conclusion that this evidence supports?
1. AThe two variables are correlated, but neither is shown to cause the other
2. BIce cream sales cause swimming pool visits
3. CSwimming pool visits cause ice cream sales
4. DThe data set must contain an error
Show answer
Answer: A. The two variables are correlated, but neither is shown to cause the other
A pattern found in observational data establishes an association, not a mechanism, and a third factor such as warm weather can drive both variables at once. Large data sets make correlations easier to find, which makes the causal caution more important rather than less.
Key insight: Correlation found in data is a starting point for investigation, not evidence of causation.

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