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Product & Business Modeling

Business Problem Framing

8 questions

Multiple Choice

Identifying ML-Suitable Problems

ML vs. Rule-Based Solutions

Feasibility Assessment

Defining Prediction Targets

Open-Ended

Converting Business Problems to ML Problems

Defining Success Criteria for ML Projects

ML Project Feasibility Assessment

When to Use ML vs Heuristics

Metrics & KPIs

8 questions

Multiple Choice

Choosing the Right Success Metric

Proxy Metrics and Their Limitations

Detecting Metric Gaming

Multi-Objective Optimization Trade-offs

Open-Ended

Choosing the Right Success Metric

Proxy Metrics and Their Pitfalls

Preventing Metric Gaming

Multi-Objective Optimization in ML Products

Experimentation & A/B Testing

8 questions

Multiple Choice

A/B Test Sample Size

Interpreting Non-Significant Results

Guardrail Metrics in Experiments

Experiment Interference

Open-Ended

A/B Testing Fundamentals for ML Models

Statistical Significance in Practice

Guardrail Metrics in Experimentation

Experiment Design Challenges

Cost-Benefit Analysis

8 questions

Multiple Choice

Asymmetric Error Costs

ML Project ROI Estimation

Resource Allocation Trade-offs

Build vs. Buy Decision

Open-Ended

Calculating ROI of ML Solutions

Make vs Buy Decisions for ML

Asymmetric Cost of Errors

Resource Allocation for ML Projects

Stakeholder Communication

8 questions

Multiple Choice

Explaining Model Performance to Executives

Communicating Model Uncertainty

Managing Stakeholder Expectations

Explaining Model Failures

Open-Ended

Explaining ML to Non-Technical Stakeholders

Model Documentation and Model Cards

Communicating Model Uncertainty

Building Trust in ML Systems

ML Product Lifecycle

8 questions

Multiple Choice

ML MVP Strategy

Model Iteration Strategy

Model Deprecation Decisions

Managing ML Technical Debt

Open-Ended

MVP Approach for ML Products

Iterative Improvement Strategy

ML Technical Debt

Model Deprecation Decisions