---
title: "AP Computer Science Principles Unit 5 Practice Test"
description: "A timed AP Computer Science Principles Unit 5 (Impact of Computing) practice test in the AP format, drawn from 1,392 multiple-choice questions written for the unit. The standard version takes about 34 minutes."
canonical: "https://fiveable.me/ap-comp-sci-p/unit-test/5"
type: "unit-test"
subject: "AP Computer Science Principles"
unit: "Unit 5"
---

# AP Computer Science Principles Unit 5 Practice Test

## Overview

A timed AP Computer Science Principles Unit 5 (Impact of Computing) practice test in the AP format, drawn from 1,392 multiple-choice questions written for the unit. The standard version takes about 34 minutes.

## Test Format

- 20 Multiple Choice Questions (34 minutes)

## Practice Questions

### Question 1

**Hospital Patient Records System**

> A hospital network uses a software system to manage patient medical records. The system was recently upgraded.
>
> - In the original system, patient records were maintained on paper charts stored in filing cabinets at each hospital location. Doctors wrote notes by hand, and records were transported physically between departments. Patients requesting copies of their records submitted a written form and waited up to two weeks.
>
> - The upgraded system stores all patient information electronically, including diagnoses, prescribed medications, lab results, imaging scans, insurance information, and emergency contact details. Any authorized healthcare provider within the hospital network can access a patient's complete record from any location.
>
> - The upgraded system (but not the original system) also records which healthcare providers accessed each patient's record, the time of access, and what information was viewed or modified.

Which of the following is the MOST likely data privacy concern introduced by the upgraded system's design?

- (A) Authorized staff might view sensitive records of patients they are not actively treating.
- (B) The centralized database prevents patients from obtaining physical copies of their own records.
- (C) The system automatically shares patient diagnoses with unauthorized external insurance companies.
- (D) Healthcare providers are unable to modify patient records once they are initially entered.

**Answer: (A)** Evaluating the legal and ethical implications of the system requires analyzing its access controls. The passage states that "any authorized healthcare provider... can access a patient's complete record from any location." This violates the principle of least privilege. Even though the system logs access, the architectural decision to allow universal access across the network creates a severe privacy risk, as curious or malicious staff can access records of neighbors, celebrities, or colleagues without a medical need.

### Question 2

A community group plans to develop a mobile application to crowdsource a map of wheelchair-accessible ramps in a city. The specific purpose is to provide reliable, verified navigation routes for wheelchair users. Which data collection feature best supports the reliability required for this purpose?

- (A) Users upload photos of ramps tagged with GPS coordinates to verify existence and location.
- (B) Users submit text descriptions of ramps without location data to protect privacy.
- (C) Users rate the aesthetic quality of ramps on a five-star scale to encourage design.
- (D) Users vote on proposed ramp locations to influence future city planning decisions.

**Answer: (A)** Connecting data verification with navigation reliability is key here. Visual evidence combined with precise geolocation ensures that the navigation data is accurate and actionable for users.

### Question 3

A company develops an automated hiring tool trained on resumes from its current employees, who are predominantly male. Which of the following describes the most likely negative impact of using this tool to screen future applicants?

- (A) The tool may unfairly penalize female applicants because the training data reflects existing gender imbalances.
- (B) The tool may unfairly penalize female applicants because the training data lacks sufficient volume to identify meaningful patterns.
- (C) The tool may unfairly penalize female applicants because the training data was not cleaned to remove duplicate or contradictory entries.
- (D) The tool may unfairly penalize female applicants because the training data combines multiple sources with different collection methods and standards.

**Answer: (A)** Connecting the source of training data to the output reveals that bias in the input (predominantly male resumes) leads to bias in the algorithm's decisions.

### Question 4

A development team is creating a health-tracking app for a global user base. Which strategy would most effectively help the team identify and reduce potential cultural biases in the interface design?

- (A) Recruiting team members with diverse backgrounds to review the design
- (B) Conducting beta testing exclusively with the development team members
- (C) Relying on the project manager's intuition to identify cultural issues
- (D) Using a standard color palette that is popular in the developers' region

**Answer: (A)** Evaluating strategies for bias reduction requires recognizing the value of diverse perspectives. A diverse team is most likely to spot cultural assumptions or exclusions that a homogeneous team might miss.

### Question 5

A team is developing a facial recognition system for a security gate and wants to ensure the system works equally well for people of all ethnicities. Which development approach best achieves this goal?

- (A) Collaborating with a diverse group of testers to collect and validate training data
- (B) Increasing the resolution of the camera hardware to capture significantly more detail
- (C) Using a larger dataset composed entirely of the company's internal employees
- (D) Optimizing the facial recognition algorithm to process images significantly faster

**Answer: (A)** Recognizing that bias often stems from non-representative data sets allows the team to design a collection method that actively includes diverse perspectives.

### Question 6

A bank is automating its loan approval process. To prevent the algorithm from discriminating against applicants from specific zip codes, which design approach is most appropriate?

- (A) Auditing the training data for historical biases and involving ethicists in the design
- (B) Removing all human oversight from the approval process to ensure mathematical objectivity
- (C) Using the zip code as the primary weighting factor to ensure geographic consistency
- (D) Increasing the complexity of the neural network to find more subtle patterns in the data

**Answer: (A)** Proactively identifying bias in data and collaborating with experts on ethics addresses the root cause of algorithmic discrimination.

### Question 7

A company develops a smart speaker that records audio only after a "wake word" is detected to improve user convenience. To address concerns that this feature might unintentionally violate user privacy, which question is most important to investigate?

- (A) How frequently does the device falsely identify the wake word in conversation?
- (B) How many different languages can the device successfully translate in real time?
- (C) How much storage space is required to save the user's preferred settings?
- (D) How fast can the device process a command after the wake word is detected?

**Answer: (A)** Investigating false positives reveals how often the device records audio without user consent. Frequent accidental activation would undermine the privacy protection intended by the wake word mechanism.

### Question 8

A software team trains a machine learning model to filter job applications, aiming to reduce the time recruiters spend on initial screening. Which investigative step is most critical to prevent the tool from perpetuating existing hiring discrimination?

- (A) Verifying that the training data represents a diverse range of successful candidates
- (B) Verifying that the software interface is compatible with all recruiter devices
- (C) Verifying that the algorithm processes applications faster than human recruiters
- (D) Verifying that the storage database is encrypted to prevent external data breaches

**Answer: (A)** Investigating the training data for representation is essential because machine learning models learn patterns from historical data; if the data reflects past biases, the model will automate discrimination.

### Question 9

A healthcare provider plans to release a mobile app that allows patients to consult doctors via video, aiming to improve access for rural communities. Which constraint related to the digital divide should be investigated first to ensure the app achieves its goal?

- (A) The availability of high-speed internet connections in the target rural areas
- (B) The availability of high-definition cameras on the doctors' office computers
- (C) The availability of secure cloud storage for archiving the video consultations
- (D) The availability of technical support staff to assist doctors with the software

**Answer: (A)** Investigating infrastructure constraints is crucial because video streaming requires high bandwidth; if the target population lacks this access, the innovation cannot function for them.

### Question 10

A social media platform updates its recommendation algorithm to prioritize content that generates high user engagement. To investigate whether this change causes unintended social harm, which metric should developers monitor?

- (A) The spread of misinformation compared to the increase in time spent on the site
- (B) The speed at which the server processes user requests during peak usage hours
- (C) The number of new user accounts created since the algorithm update was released
- (D) The amount of revenue generated from advertisements displayed on popular posts

**Answer: (A)** Monitoring the correlation between engagement and misinformation helps determine if the algorithm is amplifying harmful content to achieve its goal of keeping users online.

## Review the Unit

- [5.1 Beneficial and Harmful Effects](/ap-comp-sci-p/unit-5/beneficial-harmful-effects/study-guide/rErWKPcu55DLj7N7L8hZ)
- [5.2 Digital Divide](/ap-comp-sci-p/unit-5/digital-divide/study-guide/cPITNOBetOdsC1E7wfig)
- [5.3 Computing Bias](/ap-comp-sci-p/unit-5/computing-bias/study-guide/Wn6X4YFxicWX7hJcjAlq)
- [5.4 Crowdsourcing](/ap-comp-sci-p/unit-5/crowdsourcing/study-guide/DEDLDePHCG22EMDaFXhh)
- [5.5 Legal and Ethical Concerns](/ap-comp-sci-p/unit-5/legal-ethical-concerns/study-guide/dAf2KQxLsqwN3aEvajq0)
- [5.6 Safe Computing](/ap-comp-sci-p/unit-5/safe-computing/study-guide/zMi0PutBHnDjIlOB5lMs)
- [Big Idea 5 Overview: Impact of Computing](/ap-comp-sci-p/unit-5/review/study-guide/5aed2VkfxMBYFkBVCEhn)

## Other Unit Tests

- [Unit 1: Creative Development](/ap-comp-sci-p/unit-test/1)
- [Unit 2: Data](/ap-comp-sci-p/unit-test/2)
- [Unit 3: Algorithms and Programming](/ap-comp-sci-p/unit-test/3)
- [Unit 4: Computer Systems & Networks](/ap-comp-sci-p/unit-test/4)

## About This Document

Canonical Fiveable pages are available as Markdown at the same path plus `.md`.

- [llms.txt](https://fiveable.me/llms.txt): index of Fiveable's sections and URL patterns
- [llms-full.txt](https://fiveable.me/llms-full.txt): complete subject and unit listing
- [MCP server](https://fiveable.me/mcp): call Fiveable as tools instead of fetching pages (`https://fiveable.me/api/mcp`)
- [MCP server for AP teachers](https://fiveable.me/mcp/teachers): a teacher's classes, assignments and AP-rubric grading (`https://fiveable.me/api/mcp/teacher`)