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1. What is a key difference between traditional software and AI products?
A) AI products always provide consistent results like traditional software when rules are well-defined. B) Traditional software evolves unpredictably, whereas AI products provide static and predictable outputs. C) Traditional software adapts to its environment, while AI products operate exactly as coded. D) Traditional software behaves as coded, while AI products learn from data and produce probabilistic outcomes.
2. What drives the "Data Flywheel" in AI product development?
A) A feedback loop involving data collection, model improvement, and product performance, which drives user growth. B) Adding more features to the product to attract more users and increase short-term engagement. C) Incrementally improving AI models by gathering more user feedback and refining the user experience. D) A strategy to collect as much data as possible without considering its quality
3. What is the purpose of AI problem framing?
A) To define rigid rules for AI development without iterative feedback. B) To break down the problem into AI tasks, engage stakeholders, and ensure Responsible AI principles are applied. C) To determine which AI tools are trendy and align with market demand. D) To create user journeys that test various pre-trained models randomly.
4. What does the "User-First Approach" in AI design emphasize?
A) Developing fault-tolerant systems to manage errors without prioritizing user satisfaction. B) Focusing on model metrics over user satisfaction to ensure optimal technical performance. C) Seamless integration with existing workflows and iterative feedback for long-term alignment. D) Creating end-to-end automation pipelines without engaging users.
5. What is a Perceptron in the context of AI?
A) A tool to directly predict outcomes without requiring training. B) A complex system used to evaluate non-linear patterns in deep learning. C) A simple model of a biological neuron and the fundamental unit of a neural network. D) A visualization method to explain model weights.
6. What is the role of gradient descent in AI model training?
A) It is an iterative optimization algorithm that minimizes the cost function by adjusting weights. B) It ensures the model generates predictions with 100% accuracy. C) It finds the maximum value of the cost function to enhance model performance. D) It adjusts layers manually based on expert intuition.
7. What is the “Data Flywheel” in AI product development?
A) A strategy to collect as much data as possible without considering its quality. B) A feedback loop involving data collection, model improvement, and product performance, which drives user growth. C) A process of replacing old data with new data to maintain relevance. D) Creating dashboards to visualize data without model retraining.
8. How does the “long-tail” affect AI product development?
A) The long-tail contains a significant portion of rare inputs, which demand extensive data and effort to handle effectively. B) The long-tail can be ignored in favor of focusing on the most common patterns to optimize resources. C) The long-tail represents a small, insignificant portion of user inputs, requiring minimal attention. D) The long-tail impacts only backend infrastructure and not product performance.
9. What does “Responsible AI” imply in product development?
A) Ensuring that AI features generate as much engagement as possible. B) Following ethical practices, reducing bias, and aligning with user expectations. C) Designing A/B tests for AI behavior to increase KPIs. D) Optimizing models for higher precision, regardless of use context.
10. How can product managers collaborate effectively with AI teams?
A) By staying focused on metrics like precision/recall and leaving product intuition to engineers. B) By aligning on success criteria, clarifying trade-offs, and managing risks jointly across disciplines. C) By adjusting user-facing flows after the model is finalized. D) By creating tasks lists and handing them to the data science team.
