CDSP 1 Quiz

1. Which of the following best describes scope creep in a data science project?

A: The addition of optional deliverables that enhance the project's output.
B: The process of minimizing the project's scope to meet deadlines.
C: The uncontrolled expansion of the project's scope beyond its limits.
D: The clear outlining of project resources and timelines.

2. What is the purpose of a Minimum Viable Product (MVP) in a data science project?

A: To present the final polished version of a product to all stakeholders.
B: To prove that a product can be developed, even if it’s not functional yet.
C: To demonstrate a working version of a product that can be iteratively improved with user feedback.
D: To collect and analyze data to determine the project’s feasibility.

3. What is the main driver of business success in data science?

A: Financial resources
B: Human resources
C: Data
D: Marketing strategies

4. What is design thinking focused on?

A: Following current design trends
B: Empathizing with user needs and innovation
C: Relying on previous design successes
D: Using traditional business methods

5. What is a key function of design thinking in data science?

A: It promotes preconceived notions.
B: It limits the scope of innovation.
C: It asks questions to discover neglected needs.
D: It reduces the importance of customer feedback.

6. What is scope creep in a data science project?

A: The process of collecting and analyzing data
B: The expansion of a project beyond its constraints
C: The method of identifying stakeholders
D: The analysis of deliverables in a project

7. Which of the following is a mandatory deliverable in a data science project?

A: Low-priority features
B: Product-recommendation system
C: Optional website demonstration
D: Marketing report

8. What is the role of stakeholders in a data science project?

A: To make technical decisions about hardware
B: To provide input on project milestones and expectations
C: To dictate the entire project scope
D: To conduct data analysis

9. What is the goal of a Proof of Concept (POC) in data science?

A: To develop a fully functioning product
B: To show that a project can be developed
C: To collect all necessary data
D: To evaluate marketing strategies

10. What is the key difference between a Minimum Viable Product (MVP) and a Proof of Concept (POC)?

A: An MVP is polished and ready for full launch, while a POC is still conceptual.
B: An MVP is meant for feedback from early adopters, while a POC proves feasibility.
C: A POC is used for public feedback, while an MVP is an internal demonstration.
D: An MVP is smaller in scope than a POC.

11. Why is data governance important in data science projects?

A: It limits data access to only senior team members.
B: It ensures that data usage complies with laws and stakeholder requirements.
C: It allows stakeholders to manipulate data freely.
D: It is only necessary for large organizations.

12. What is the role of informed consent in data science?

A: To keep data collection methods secret
B: To ensure individuals understand how their data will be used
C: To speed up data collection
D: To avoid asking individuals for permission to use their data

13. Which of the following best defines data democratization?

A: Making data accessible to all employees regardless of technical skill
B: Restricting access to data for senior-level stakeholders
C: Allowing only data scientists to interpret data
D: Focusing on data security rather than accessibility

14. What is a key element of making data understandable in data democratization?

A: Providing interactive dashboards
B: Limiting data access to avoid confusion
C: Creating complex algorithms
D: Sharing raw data without context

15. Which of the following is a common outcome of data science?

A: Creating promotional materials
B: Conducting customer surveys
C: Performing classification and regression tasks
D: Planning marketing events

16. What is the purpose of classification in data science?

A: To determine the value of a numeric variable
B: To group data without prior knowledge
C: To identify the class a new instance of data belongs to
D: To make data more accessible

17. Which type of data science model focuses on estimating the value of a numeric variable?

A: Classification
B: Regression
C: Clustering
D: Reinforcement

18. What is randomness in data science models?

A: The certainty that all predictions will be correct
B: The existence of unknown factors in data and algorithms
C: The deterministic nature of model outcomes
D: The ability to predict outcomes with full accuracy

19. Which of the following is a guideline for initiating a data science project?

A: Begin without consulting stakeholders
B: Focus on only one type of data analysis method
C: Be prepared to adapt the project when data changes
D: Prioritize optional deliverables over mandatory ones

20. What should be the first step in framing a data science problem?

A: Define the specific tools to be used for analysis
B: Describe the problem in clear and actionable terms
C: Collect as much data as possible
D: Determine the final deliverables