Unit 2: Data
CS Principles · Unit 2 · Paper 3

Data unit test

A test on this unit alone, marked as a percentage and a letter grade — for the test your class is actually sitting, rather than for May. Answer everything, then submit once: seeing the answer to question 3 before attempting question 4 makes the final percentage meaningless.

Each paper is built from this unit’s 36 terms and is the same for everyone, so a teacher can assign “Unit 2, Paper 3” and every student sits the identical test. Multiple choice is marked objectively; the written sections you mark yourself against the model answer and rubric.
Suggested time 33 min 30 points0/17 attempted
1

Text as binary

2

Bit

3

Converting binary to decimal

4

Bias in a program

5

Binary number system

6

Personally identifiable information (PII)

7

Open data

8

Why digital data is discrete

9

Data cleaning

10

Data visualization

11

Lossy compression

12

Extracting information from data

Short answer 1. Define or explain: Information vs data

3 pts

Short answer 2. Define or explain: Abstraction

3 pts

Short answer 3. Define or explain: Correlation vs causation

3 pts

Short answer 4. Define or explain: Bias in data collection

3 pts

Free response

6 pts

A city transit agency collects a record for every trip taken on its buses. Each record contains the route number, the stop where the rider boarded, the time of boarding, and the fare category (adult, student, senior, reduced-fare). The agency wants to use this data to decide where to add service.

Identify one piece of information the agency could extract from this data that no single record contains, and describe the process used to extract it.

Explain the difference between the data itself and metadata, giving one example of metadata the agency might store alongside these records.

Explain one way the agency could use filtering to answer the question of which stops are busiest during the morning peak.

Explain why the agency should be cautious about concluding that a stop with few boardings has low demand.

Describe one specific privacy risk created by collecting these records, and one technique that reduces it.

Explain the difference between lossy and lossless compression, and state which would be appropriate if the agency needed to archive these records for later analysis.