Data Science & Ethics:
Summary
Topics Covered:
1. Foundations of Data Science
2. Data Collection and Data Quality
3. Data Privacy and Protection
4. Informed Consent and Data Ownership
5. Algorithmic Fairness and Bias
6. Transparency and Explainability
7. Accountability and Governance
8. Ethical Frameworks and Principles
9. Data Science in Business and Society
10.Regulation and Compliance (GDPR, AI Act)
11.Case Studies and Practical Dilemmas
Note: This is a completely original practice examination written to reflect standard
learning objectives of a university-level Data Science & Ethics course. It is not
drawn from any existing test bank, textbook, or copyrighted source. The correct
answer is bonded (bolded) after each question, with a brief rationale.
SECTION 1: FOUNDATIONS OF DATA SCIENCE (Questions 1–50)
,Section 1A: Introduction to Data Science (Questions 1–25)
1. (Recall) Data science is best defined as:
A) The study of computer hardware
B) An interdisciplinary field that uses scientific methods, algorithms, and
systems to extract knowledge from data ✔
C) A type of database
D) A programming language
2. (Comprehension) The main components of data science include:
A) Statistics, computer science, and domain knowledge
B) Statistics, computer science, and domain knowledge ✔
C) Only statistics
D) Only programming
3. (Recall) The data science workflow typically includes:
A) Data collection, cleaning, analysis, modeling, and communication
B) Data collection, cleaning, analysis, modeling, and communication ✔
C) Only modeling
D) Only analysis
4. (Comprehension) The main goal of data science is to:
A) Store large amounts of data
B) Extract actionable insights to support decision-making ✔
C) Replace human judgment
D) Automate all processes
5. (Application) A company using customer data to predict churn is applying:
A) Data science ✔
B) Accounting
C) Marketing only
D) No method
6. (Analysis) The main difference between data science and traditional statistics is:
A) No difference
,B) Data science emphasizes prediction and large-scale data; statistics
emphasizes inference ✔
C) Statistics uses no data
D) Data science uses no data
7. (Recall) Which of the following is NOT a typical data science role?
A) Data scientist
B) Data engineer
C) Machine learning engineer
D) Hardware technician ✔
8. (Comprehension) A data engineer primarily:
A) Builds models
B) Builds and maintains data infrastructure ✔
C) Communicates insights
D) Manages ethics
9. (Application) A professional designing data pipelines is a:
A) Data engineer ✔
B) Data scientist
C) ML engineer
D) Business analyst
10. (Analysis) The main challenge of data science in practice is:
A) Too much data
B) Data quality, bias, and ethical concerns ✔
C) Too few tools
D) No challenges
11. (Recall) The "data lifecycle" includes:
A) Collection, storage, processing, analysis, and disposal
B) Collection, storage, processing, analysis, and disposal ✔
C) Only collection
D) Only analysis
, 12. (Comprehension) Data governance refers to:
A) Managing data as an asset with policies and standards
B) Managing data as an asset with policies and standards ✔
C) Storing data only
D) Ignoring data
13. (Application) A company implementing data governance is:
A) Establishing policies for data management ✔
B) Ignoring data
C) Storing data
D) No action
14. (Analysis) The main benefit of data governance is:
A) Increased data quality and compliance
B) Increased data quality and compliance ✔
C) Reduced data quality
D) No benefit
15. (Recall) Big data is characterized by:
A) Volume, velocity, variety, and veracity
B) Volume, velocity, variety, and veracity ✔
C) Only volume
D) Only velocity
16. (Comprehension) The "variety" of big data refers to:
A) Different types of data (structured, unstructured)
B) Different types of data (structured, unstructured) ✔
C) The speed of data
D) The size of data
17. (Application) A company analyzing social media posts is dealing with:
A) Unstructured data ✔
B) Structured data
C) No data
D) Only numerical data
Summary
Topics Covered:
1. Foundations of Data Science
2. Data Collection and Data Quality
3. Data Privacy and Protection
4. Informed Consent and Data Ownership
5. Algorithmic Fairness and Bias
6. Transparency and Explainability
7. Accountability and Governance
8. Ethical Frameworks and Principles
9. Data Science in Business and Society
10.Regulation and Compliance (GDPR, AI Act)
11.Case Studies and Practical Dilemmas
Note: This is a completely original practice examination written to reflect standard
learning objectives of a university-level Data Science & Ethics course. It is not
drawn from any existing test bank, textbook, or copyrighted source. The correct
answer is bonded (bolded) after each question, with a brief rationale.
SECTION 1: FOUNDATIONS OF DATA SCIENCE (Questions 1–50)
,Section 1A: Introduction to Data Science (Questions 1–25)
1. (Recall) Data science is best defined as:
A) The study of computer hardware
B) An interdisciplinary field that uses scientific methods, algorithms, and
systems to extract knowledge from data ✔
C) A type of database
D) A programming language
2. (Comprehension) The main components of data science include:
A) Statistics, computer science, and domain knowledge
B) Statistics, computer science, and domain knowledge ✔
C) Only statistics
D) Only programming
3. (Recall) The data science workflow typically includes:
A) Data collection, cleaning, analysis, modeling, and communication
B) Data collection, cleaning, analysis, modeling, and communication ✔
C) Only modeling
D) Only analysis
4. (Comprehension) The main goal of data science is to:
A) Store large amounts of data
B) Extract actionable insights to support decision-making ✔
C) Replace human judgment
D) Automate all processes
5. (Application) A company using customer data to predict churn is applying:
A) Data science ✔
B) Accounting
C) Marketing only
D) No method
6. (Analysis) The main difference between data science and traditional statistics is:
A) No difference
,B) Data science emphasizes prediction and large-scale data; statistics
emphasizes inference ✔
C) Statistics uses no data
D) Data science uses no data
7. (Recall) Which of the following is NOT a typical data science role?
A) Data scientist
B) Data engineer
C) Machine learning engineer
D) Hardware technician ✔
8. (Comprehension) A data engineer primarily:
A) Builds models
B) Builds and maintains data infrastructure ✔
C) Communicates insights
D) Manages ethics
9. (Application) A professional designing data pipelines is a:
A) Data engineer ✔
B) Data scientist
C) ML engineer
D) Business analyst
10. (Analysis) The main challenge of data science in practice is:
A) Too much data
B) Data quality, bias, and ethical concerns ✔
C) Too few tools
D) No challenges
11. (Recall) The "data lifecycle" includes:
A) Collection, storage, processing, analysis, and disposal
B) Collection, storage, processing, analysis, and disposal ✔
C) Only collection
D) Only analysis
, 12. (Comprehension) Data governance refers to:
A) Managing data as an asset with policies and standards
B) Managing data as an asset with policies and standards ✔
C) Storing data only
D) Ignoring data
13. (Application) A company implementing data governance is:
A) Establishing policies for data management ✔
B) Ignoring data
C) Storing data
D) No action
14. (Analysis) The main benefit of data governance is:
A) Increased data quality and compliance
B) Increased data quality and compliance ✔
C) Reduced data quality
D) No benefit
15. (Recall) Big data is characterized by:
A) Volume, velocity, variety, and veracity
B) Volume, velocity, variety, and veracity ✔
C) Only volume
D) Only velocity
16. (Comprehension) The "variety" of big data refers to:
A) Different types of data (structured, unstructured)
B) Different types of data (structured, unstructured) ✔
C) The speed of data
D) The size of data
17. (Application) A company analyzing social media posts is dealing with:
A) Unstructured data ✔
B) Structured data
C) No data
D) Only numerical data
