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WGU Ethics In Technology (D333) Exam Questions
- Topic 1: Ethics in Technology: This section of the exam measures skills of IT Support Specialists and covers the ethical considerations involved in using technology in modern society. Learners are introduced to decision-making processes guided by ethical frameworks and study real-world cases involving surveillance, social media, hacking, data manipulation, plagiarism, piracy, artificial intelligence, responsible innovation, and the digital divide.
- Topic 2: Implementing ethical decision-making frameworks in the information age: This section of the exam measures skills of IT Support Specialists and explains how learners apply ethical frameworks to make informed decisions in technology use. It introduces methods for approaching complex scenarios while considering moral and societal impacts.
- Topic 3: Describing ethical issues regarding data privacy, accuracy, access, and security: This section of the exam measures skills of IT Compliance Technicians and highlights ethical concerns related to managing data. Learners understand the importance of data privacy, accuracy, proper access, and maintaining security in organizational and personal contexts.
- Topic 4: Explaining professional ethical codes and their role in guiding professional behavior: This section of the exam measures skills of IT Support Specialists and explains how professional codes of ethics shape responsible behavior in technology fields. Learners gain insight into standards that guide conduct and decision-making in professional settings.
- Topic 5: Identifying interventions for personal bias and related legal concerns This section of the exam measures skills of IT Compliance Technicians and focuses on recognizing personal biases and understanding their legal implications. Learners learn how to apply interventions to ensure ethical, fair, and lawful practices in technology use.
Free WGU Ethics In Technology (D333) Exam Actual Questions
Note: Premium Questions for Ethics In Technology (D333) were last updated On Oct. 09, 2026 (see below)
A company builds a new artificial intelligence (Al) system that helps address drug and alcohol use disorders by developing an individualized plan to support people with one of these disorders. The system developers load nationwide information from the last 20 years to provide a baseline for the Al to analyze. Upon activating the system, the company receives complaints about bias against minorities with a drug or alcohol use disorder.
What introduced bias into the system?
A skewed sample occurs when historical data is biased, unrepresentative, or imbalanced, leading to discriminatory AI outcomes. In this case, the AI system was trained on 20 years of national data, which likely contained historical biases against minorities in drug and alcohol disorder treatments.
Since past law enforcement and healthcare policies have disproportionately impacted minority groups, the AI inherited these biases and began making unfair, discriminatory predictions.
Relevant Ethical Reference in Technology:
Algorithmic Bias & AI Ethics (ACM/IEEE Guidelines) -- AI models must be trained on diverse and representative data.
Fairness in Machine Learning (FAT/ML Framework) -- Preventing bias in AI requires identifying and correcting skewed data.
Historical Bias & Systemic Discrimination -- Training AI on biased past records leads to unethical and discriminatory outcomes.
Deontological Ethics (Fair AI Decision-Making) -- AI developers must ensure unbiased, ethical, and fair AI outputs.
Thus, the correct answer is B. Skewed sample, as the AI model was trained on biased historical data, leading to discrimination.
A company is bidding on a website project for a prospective client. The company knows that the client is going to weigh the time to deliver the project as one of its primary decision factors. The client has advised all bidders that completion time should be no more than three months with a budget of S50.000.
The company knows that with its current resourcing, it cannot complete the project in less than four months. However, it is confident that it will be able to deliver within four months to a high-quality standard and 20% under the set budget. The company advises the client that it would need four months but would be able to complete the project under budget.
What is this behavior considered?
The company honestly discloses its realistic project timeline to the client without misleading or misrepresenting its capabilities.
Why is this ethical?
The company does not engage in deception; it clearly states that it requires four months rather than falsely promising three.
It offers added value by completing the project 20% under budget, which is beneficial for the client.
This aligns with ethical principles of honesty, transparency, and fairness.
Why is this legal?
There is no law requiring the company to meet the three-month deadline if it discloses its timeline honestly.
Misrepresentation would be illegal, but the company truthfully communicates its capabilities.
Why Not the Other Options?
A . Ethical and Illegal: There is no violation of any law.
B . Unethical and Illegal: The company does not engage in fraud or deception.
C . Unethical and Legal: The action is not unethical since it follows principles of honesty and integrity.
Thus, the correct answer is D. Ethical and Legal because the company acts transparently while complying with business ethics and legal standards.
Reference in Ethics in Technology:
ACM Code of Ethics (2022).
IEEE Code of Conduct (2020).
Brenkert, G. G. (2008). 'Marketing Ethics.' Blackwell Encyclopedia of Management.
A cloud computing company uses machine learning software to screen the resumes of job seekers. The company's aim is to reduce potential human prejudice in the hiring process. To set up the software and train the machine learning model, the company provides information from its hiring decisions over the past five years. After several months of using the software, the company runs an audit and finds that the software screens out minority job seekers at a much higher rate.
Which behavior is the trained machine learning model displaying?
The machine learning model unintentionally discriminates against minority job seekers because it was trained on historical hiring data that contained biases.
Why Exposing Bias in Data?
Machine learning systems reflect the biases present in their training data.
If past hiring decisions favored certain groups, the AI will replicate and reinforce those biases.
The audit revealed systematic discrimination, indicating bias in the data, not the AI itself.
Why Not the Other Options?
B . Acting fairly toward candidates -- The AI is not fair if it disproportionately excludes minority job seekers.
C . Exhibiting intent to harm applicants -- The AI does not have intent; bias is an unintended consequence of flawed data.
D . Scoring low humility measurement -- 'Humility' is not a standard AI metric; bias exposure is the actual issue.
Thus, the correct answer is A. Exposing bias that exists in the data, as the AI amplifies historical discrimination in hiring.
Reference in Ethics in Technology:
O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
The retail store manager of a telecommunications service provider has been manually entering the data record of device returns into the company's device database every time a subscriber swaps their old phone for a new one. A recent internal audit revealed that several device records are missing from the device database.
Which aspect of data management should the store employ to avoid this situation?
The missing records in the device database indicate that the store needs to improve the accuracy of its data management processes.
Why Accuracy?
Data accuracy ensures that all records are correctly entered, complete, and reliable.
Manual data entry is prone to errors, including missing or incorrect records.
Implementing automated data entry or validation checks can reduce human error and ensure the integrity of the database.
Why Not the Other Options?
A . Encryption -- Protects data from unauthorized access but does not ensure correct data entry.
B . Collection -- Refers to gathering data, but the issue here is with missing or incorrect entries.
C . Availability -- Ensures data can be accessed but does not guarantee its accuracy.
Thus, the correct answer is D. Accuracy, as it directly addresses the issue of missing records.
Reference in Ethics in Technology:
Redman, T. C. (1998). The Impact of Poor Data Quality on the Typical Enterprise.
Tavani, H. T. (2016). Ethics and Technology: Controversies, Questions, and Strategies for Ethical Computing.
Data Management Body of Knowledge (DMBOK) (2020).
An organization plans to hire three external software engineers to build client software to connect its lightweight wireless devices to the cloud. It will do so according to specific terms and a specific duration defined in a legal document. The duration of the work will be six months.
Which workforce category should the organization hire within?
Since the organization is hiring external software engineers for a fixed six-month project, independent contractors are the best option.
Why Independent Contractors?
They work on a project basis, as defined in a legal contract.
The organization does not provide full-time benefits.
Contractors are specialists hired for short-term needs, making them ideal for temporary projects.
Why Not the Other Options?
A . Full-time employees: Not suitable, as the work is temporary (six months only).
B . In-house expertise: The company is hiring external workers, not relying on existing staff.
C . Part-time salaried workers: Part-time employees receive benefits, while contractors do not.
Thus, the correct answer is D. Independent contractors, as they are ideal for temporary project-based work.
Reference in Ethics in Technology:
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