IVQ 601-650
Section 61: Synthetic Data & Simulation (10 Questions)
How do you use GenAI to generate high-quality synthetic tabular data?
What are key metrics to evaluate utility vs. privacy in synthetic datasets?
How would you simulate edge-case user behavior using GenAI?
How do you combine GenAI with real-world data to augment training?
What are regulatory implications of using synthetic data for training LLMs?
How would you use GenAI to create synthetic legal contracts or forms?
What’s the role of GenAI in simulating user journeys for UI/UX testing?
How do you detect bias or drift in synthetic datasets?
What tools help you label or classify synthetic samples efficiently?
How can you use GenAI to simulate customer support transcripts for chatbot training?
Section 62: GenAI for Data Analytics & Business Intelligence (10 Questions)
How do you build a GenAI assistant that translates natural language into SQL?
What are best practices for validating GenAI-generated queries against schema?
How do you audit GenAI for incorrect aggregations or filter logic in BI queries?
What are safe prompt patterns for financial, KPI, or operations dashboards?
How do you use embeddings to enable question-answering over business reports?
How can GenAI help democratize data access for non-technical stakeholders?
What’s your approach to caching and reranking GenAI-generated insights?
How do you integrate GenAI with existing BI tools like Looker or Power BI?
How do you tune prompts for consistency in numerical output from LLMs?
How would you use a vector store to support ad hoc business analytics?
Section 63: Active Learning & Feedback Loops (10 Questions)
What is active learning and how can GenAI systems use it post-deployment?
How do you select samples for human-in-the-loop feedback efficiently?
What’s the role of uncertainty estimation in triggering active learning?
How can GenAI auto-label low-confidence examples for later review?
How do you manage feedback queue prioritization in production apps?
What are metrics to determine convergence in human-AI retraining cycles?
How do you monitor overfitting to feedback signals in chat applications?
How can you embed feedback directly into prompt routing or flow logic?
How do you separate quality control from preference feedback in GenAI systems?
How would you crowdsource reinforcement data safely and ethically?
Section 64: Multi-Agent Collaboration & Role Design (10 Questions)
How do you coordinate multiple LLM agents working on a single task?
What communication protocol or memory structure enables inter-agent reasoning?
How do you prevent conflicting tool calls in a collaborative agent team?
What are the benefits of using different LLMs for different agent roles?
How do you design a QA checker agent that validates another agent’s output?
What’s the role of arbitration or controller agents in agent networks?
How would you handle asynchronous workflows among agents?
What memory architectures support group-level task memory vs. agent-specific memory?
How do you resolve intent clashes in multi-agent goal pursuit?
How can agents be trained to specialize based on task patterns over time?
Section 65: Frontier Innovation & Emerging Techniques (10 Questions)
What are retrievers-as-agents and how do they evolve traditional RAG systems?
How do you evaluate performance in autonomous agent benchmarks like AgentBench or SWE-bench?
What is the role of synthetic reflection in agent self-improvement?
What is grounding via tool calls, and how does it improve factuality?
How can GenAI be fused with reinforcement learning in dynamic environments?
What is continual pretraining and how does it affect long-term model evolution?
How can models be made contextually aware of multi-modal sensor data in robotics?
What are attention bottlenecks and how do architectures like FlashAttention solve them?
What trends are you seeing in small-model distillation with frontier-scale capabilities?
How do you imagine GenAI + graph neural networks working together in future workflows?
Last updated