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Hands-on Exercises
Chapter 13 Exercises
Scaling for Growth - AI-Powered Growth Strategies
Complete these exercises to reinforce your learning
Knowledge Quiz (5 Questions)
Practical Exercise
Chapter 13 Knowledge Check
Test your understanding of the key concepts from this chapter
Question 1: What is a primary challenge in AI product roadmapping compared to traditional software?
A
Shorter development cycles
B
High degree of uncertainty in R&D outcomes
C
Less need for data infrastructure
D
Simpler dependency management
Question 2: When creating an AI product roadmap, what does the "dual-track" approach refer to?
A
Running marketing and sales tracks in parallel
B
Developing the user interface and backend simultaneously
C
Separating the discovery/research track from the delivery/engineering track
D
A/B testing two different AI models at the same time
Question 3: Which of the following is a key component of an AI product roadmap that is less common in non-AI products?
A
User stories
B
Feature release dates
C
Data acquisition and annotation strategy
D
Marketing launch plan
Question 4: How should a product manager prioritize features on an AI roadmap?
A
Based solely on the potential for revenue increase
B
By focusing on the easiest-to-implement AI models first
C
Using a framework that balances user value, technical feasibility, and data availability
D
Prioritizing features requested by the most senior stakeholders
Question 5: What is the role of "model-driven" vs "data-driven" thinking in AI roadmapping?
A
Model-driven focuses on improving the algorithm, while data-driven focuses on acquiring more data.
B
Model-driven is for research, data-driven is for production.
C
They are competing methodologies for AI development.
D
Model-driven thinking is obsolete and has been replaced by data-driven approaches.
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