AI Product Manager: How many can you talk about in 100 interview questions?

This article has compiled 100 interview questions for AI product manager job seekers or practitioners, covering key dimensions such as technical understanding, product design, and commercialization, helping readers accurately grasp job requirements, improve professional quality, and calmly cope with interview challenges.

During this period, I sorted out the responsibilities and requirements of AI product manager recruitment positions in the current market, combined with my own learning practice and large model dialogue exploration, and sorted out 100 AI product manager interview questions.

If you plan to transform into an AI product manager, or you are already an AI product manager, you can take a look at these questions, how much do you understand, you can also discuss these questions with AI, simulating multiple rounds of Q&A between interviewers and candidates, which is a learning method in the AI era.

If you are hiring an AI product manager, you can also look at these questions, and you can also discuss with AI, play the role of interviewer and candidate, and conduct multiple rounds of Q&A and discussion on some questions.

When I was running in the morning, I used to listen to songs and listen to books, but now, I will chat with AI, especially about artificial intelligence, and the big model knows a lot more than we know.

100 interview questions, divided into 6 dimensions and five levels of difficulty, 1 star is the easiest and 5 stars are the most difficult.

Six dimensions:

1. Technical understanding
2. Product design
3. project management
4. commercialization
5. Ethical compliance
6. Industry Insights

Difficulty Star Rating and Interview Instructions:

To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.

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Difficulty selection:

• Junior position: ★ ~ ★★★ (focus on technical understanding and basic design)
• Senior position: ★★★ ~ ★★★★ (high-end technology, commercialization, risk control)
• Expert post: ★★★★ ~★★★★★ (strategy, ethics, industry restructuring)
• Questioning skills: For high-star questions (★★★★★), candidates can be asked to draw a flow chart or give examples for detailed deduction.

Note: The above questions can be replaced with industry cases according to the specific business scenarios of the enterprise (such as finance, medical care, consumption) to enhance pertinence.

1. Technical understanding and algorithm basics (25 questions)

Examination characteristics: mastery of technical principles, algorithm application ability, and technical boundary judgment

1. Explain the core differences between machine learning, deep learning, and artificial intelligence (technical framework understanding,) ★
2. List 5 common machine learning algorithms and their typical application scenarios (e.g., KNN for recommendation systems) (algorithm applications,) ★★
3. What is the difference between supervised learning, unsupervised learning, and reinforcement learning? Give a product case (technical classification,) ★★
4. What is overfitting? How to avoid its risks from the perspective of product design? (Model Optimization,★★★)
5. How to deal with data imbalance? Examples of productized solutions (data governance) ★★★
6. Explain the principles of transfer learning and explain its application value in cross-domain AI products (technology transfer,★★★)
7. Why is model interpretability important? How to improve user trust through product design? (Interpretability design,★★★)
8. Comparing the advantages and disadvantages of SaaS model and API call model in AI commercialization (technical architecture, ★★★
9. What is Data Drift? How to monitor through product mechanisms? (Data monitoring,★★★★)
10. The core principles and landing value of large model fine-tuning technology (such as LoRA) (large model tuning,) ★★★★
11. Explain the advantages of Transformer architecture over RNN (NLP technology,) ★★★
12. Difference between model distillation and pruning and product significance (model compression,★★★★)
13. Technical Difficulties and Product Avoidance Strategies of Multimodal AI (Multimodal Design,) ★★★★
14. What are the technical boundaries of AIGC? For example, scenarios that cannot be reliably solved at present (technical limitations,) ★★★
15. How to choose the right AI model for your business scenario? What dimensions need to be considered? (Model Selection,★★★)
16. What is a cold start problem? How can I mitigate with product strategy? (Cold Start Design,★★★)
17. Principles of Federated Learning and Its Application in Privacy-Sensitive Products (Privacy Technologies, ★★★★ )
18. Comparison of applicable scenarios for real-time inference and batch processing (performance optimization,) ★★★
19. Explain the impact of AI chips (e.g., TPU) on product performance (hardware collaboration,) ★★★
20. Comparing the commercialization path selection of open source models and self-developed models (technology selection,) ★★★★
21. How to design a model effect evaluation index system? (Indicator design,) ★★★
22. What is model robustness? How to pass the test guarantee? (Robertability test,★★★★)
23. Application Scenarios and Limitations of Knowledge Graph in AI Products (Knowledge Engineering) ★★★
24. Collaborative Strategies for On-device AI and Cloud Computing (Deployment Architecture,) ★★★
25. Core Differences Between Generative AI and Discriminant AI and Product Positioning (Technology Classification,) ★★

2. Product design and demand analysis (20 questions)

Examination characteristics: user insight, demand transformation, experience design

1. How to transform user needs into AI technology needs? Take “intelligent customer service sentiment recognition” as an example (demand disassembly,) ★★★
2. Design an AI voice assistant PRD with prioritization and technical feasibility analysis (document writing, ★★★★ )
3. If the algorithm team feedback that the implementation cost of the function is too high, how to adjust the plan? (Resource trade-offs,★★★)
4. How do you balance model accuracy with user experience responsiveness? (Performance trade-offs,★★★)
5. Design an AI health monitoring product interaction logic for the elderly (age-appropriate design,) ★★★
6. How to verify the effectiveness of recommendation algorithms through A/B testing? (Experimental design,★★★)
7. Design a functional framework for cross-border e-commerce intelligent product selection tools (scenario design,) ★★★★
8. How to define the core functions of AI products? What dimensions need to be considered? (Demand focus,) ★★
9. How to handle uncertainty in AI products (e.g., fluctuations in model output)? (Fault-tolerant design,★★★)
10. How to design user experience evaluation metrics for AI products? (Experience quantification,) ★★★
11. If users report biased AI-generated content, how to optimize the product mechanism? (Fairness design,★★★★)
12. How to design a personalized experience for an AI assistant to enhance emotional connection? (Emotional design,★★★)
13. Designed an AI writing tool with paid features Differences (commercial design,★★★)
14. How to increase subscription rates through user tiering? (Layered operation,★★★)
15. Design a cold start growth plan for AI products (budget 500,000 yuan) (growth strategy,★★★★)
16. How to optimize user retention through data burial? (Data analysis,★★★)
17. If the price of competing products is reduced by 30%, how to adjust the commercialization strategy? (Competitive response,★★★★)
18. How to design a fairness guarantee mechanism for AI recruitment systems? (Ethical Design, ★★★★★)
19. Risk prevention and control plan for minors using AI companion products (compliance design,) ★★★★
20. How to reduce the information cocoon effect through product design? (Ecological Health,★★★★)

3. Project management and technical collaboration (15 questions)

Examination characteristics: cross-team collaboration, risk control, process optimization

1. Describe the whole process and key risk points of an AI project from requirements review to launch (process management, ★★★
2. How to manage collaboration conflicts between algorithm engineers and front-end engineers? (Conflict Coordination,) ★★★
3. How to supplement data with product strategy when data is insufficient? (Data acquisition,★★★)
4. How to make a version iteration plan for AI products? (Priority management,★★)
5. If the model doesn’t work as expected, how can you drive problem solving? (Problem attribution,★★★)
6. How do you design a data annotation process to balance quality and cost? (Annotation management,★★★)
7. Applicability and Adaptation of Agile Development (Scrum) in AI Project Management (Methodology,) ★★
8. How to explain the principle of large model fine-tuning to non-technical teams? (Technical communication,★★)
9. How to deal with technical bottlenecks in AI product development? (Bottleneck breakthrough,★★★★)
10. How do you coordinate the work of data scientists, engineers, and designers? (Cross-functional collaboration,) ★★★
11. How to manage the development cycle of AI products? (Cycle control,★★★)
12. How to ensure the data quality of AI products? (Quality Control Design,) ★★★
13. If the model performance deteriorates after launch, how can I troubleshoot and fix it? (Troubleshooting,★★★★)
14. How to design a data security protection mechanism for an AI product? (Safety management,★★★★)
15. How can I assess the reliability of third-party technology vendors? (Supplier Management,★★★)

4. Commercialization and operation strategy (15 questions)

Examining characteristics: profit model design, market insight, and operational execution

1. If a company requires profitability within half a year, which business model would you choose? Why? (Profit Strategy,★★★★)
2. How to assess the reasonableness of API pricing? List 3 core dimensions (pricing strategy,) ★★★
3. Design an overseas localization operation plan for AI products (global operation,) ★★★★
4. How to drive commercialization function iteration through user feedback? (User-driven,★★★)
5. Explain the specificity of “product-market matching” (PMF) in the field of AI (market validation,★★★)
6. How do you design a hybrid subscription vs. pay-as-you-go model? (Mixed monetization,★★★★)
7. How to drive traffic and achieve paid conversions with the free version? (Funnel design,★★★)
8. If a user requests to delete traces of AI-generated data, what is the technical solution? (Data Compliance,★★★★)
9. How to handle copyright disputes over AI-generated content? (Copyright Management,★★★★)
10. Designing a compliance framework for AI medical products (Medical Compliance, ★★★★★)
11. How to expand the commercialization scenarios of AI products through the partner ecosystem? (Ecological Cooperation,★★★★)
12. Design a brand influence enhancement plan for an AI product (brand operation,) ★★★
13. How to accelerate the market penetration of AI products through KOL cooperation? (Marketing Strategy,★★★)
14. How to design a customer success system for AI products? (Customer Management,★★★)
15. How to deal with business adjustments caused by sudden changes in government regulatory policies? (Policy Response, ★★★★★)

5. Ethics and Compliance and Higher-Order Thinking (15 questions)

Examination characteristics: risk awareness, ethical decision-making, strategic vision

1. How to design transparency explanations for AI products? (Interpretability,★★★)
2. GDPR Impact on AI Product Data Collection and Compliance Essentials (Data Compliance) ★★★★
3. How to assess the ethical risks of AI medical products? (Risk Assessment, ★★★★★)
4. If a user is found to be using AI tools to falsify evidence, what is the response strategy? (Risk disposal,) ★★★★
5. How do you view the balance between AI ethics and privacy protection? (Ethical trade-offs,★★★)
6. Design a long-term technology evolution roadmap for AI products (Technology Strategy,) ★★★★
7. What are the main trends in the AI industry right now? Impact on product direction? (Trend Insights,★★★)
8. What industries does AI have the potential to be disruptive? Why? (Industry prediction,★★★)
9. How to respond to the rapid changes in AI technology? (Technology iteration,★★★)
10. What do you think is the future direction of AI products? (Strategic Vision,★★★)
11. How to adapt to AI product development needs through organizational structure adjustment? (Organizational adaptation,★★★★)
12. Design a disaster recovery solution for an AI product (risk control design,) ★★★★
13. How to build a user trust system for AI products? (Trust Engineering,★★★)
14. Explain the logic of AI technology’s reconstruction of traditional industry value chains (Industry Change,★★★★)
15. How to design a social impact assessment mechanism for AI products? (Social Assessment, ★★★★★)

6. Industry Insight and Open Questions (10 questions)

Examination characteristics: industry sensitivity, innovative thinking, critical thinking

1. Analyze the impact and opportunities of ChatGPT on the existing AI product ecosystem (Competitive Product Analysis) ★★★★
2. If you were asked to redesign Kimi, what features would you optimize? (Product Criticism,★★★)
3. What are the innovation opportunities for AI products in the metaverse scenario? (Innovative Design,★★★★)
4. What are the differences in the core competencies of AI product managers in the field of autonomous driving? (Field differences,★★★)
5. How to evaluate the current game relationship between the AI open source community and closed source commercialization? (Ecological Insights,★★★★)
6. If you had an unlimited budget, how would you design a disruptive AI product? (Innovation Planning,★★★★)
7. Give an example of a failed AI product case and analyze the reasons (case review), ★★★
8. How to deal with the risk of AI technology bubbles? (Risk prediction,★★★★)
9. Design a carbon footprint assessment and optimization solution for AI products (sustainability) ★★★★
10. Summarize the core values of AI product managers in one sentence (essential thinking,) ★★

Can you talk about the above questions, welcome to discuss.