IVQ 751-800
Section 76: Observability, Logging & Debugging (10 Questions)
How do you log prompt inputs and model outputs while preserving privacy?
What are key metrics for GenAI observability in production?
How do you track response variance across model versions?
How do you build a replay system for GenAI prompt testing?
How do you detect degraded performance in LLM-based endpoints?
What’s the role of prompt versioning and rollback in enterprise GenAI systems?
How can you trace token consumption per user session or feature?
How would you implement structured tracing across prompt → tool → response?
How do you identify and debug inconsistent behavior in chat-based LLM flows?
What’s the role of LangFuse or OpenLLMetry in tracing LLMs?
Section 77: Multi-LLM Routing & Smart Switching (10 Questions)
How do you choose the right LLM for a given user query at runtime?
What factors influence routing between open-source and proprietary models?
How do you dynamically route based on cost thresholds or latency?
How would you benchmark model routing logic for accuracy and efficiency?
How can you fine-tune a classifier to route prompts to specialized LLMs?
How do you monitor performance across multiple GenAI providers (e.g., OpenAI, Anthropic)?
What’s your caching strategy when routing between Claude, GPT-4, and Mistral?
How do you build routing logic that adapts to API rate limits or outages?
What is model blending and how do you fuse responses from multiple LLMs?
How do you measure the effectiveness of your LLM router over time?
Section 78: GenAI in Education & Coaching (10 Questions)
How would you design an LLM-powered tutor for math or coding skills?
How do you personalize GenAI learning paths for different skill levels?
What are safe guardrails for GenAI tutors to prevent misinformation?
How would you handle real-time feedback and scaffolding in a GenAI coach?
How do you track learner progress using an LLM interaction history?
What’s your method for generating adaptive quizzes using LLMs?
How can GenAI assist teachers in grading, feedback, or lesson planning?
How do you align GenAI learning with national curriculum frameworks?
How do you evaluate accuracy, pedagogical soundness, and learner engagement?
What’s the future of multi-modal GenAI tutors in education?
Section 79: LLM Agent Deployment Challenges (10 Questions)
How do you debug agents that enter infinite loops in multi-step workflows?
What memory design patterns help avoid stale context issues in agents?
How do you avoid unintended side effects when agents call external tools?
What rate-limiting patterns are needed for safe agent deployment at scale?
How would you log agent decisions for downstream audit and analytics?
How do you tune agent confidence thresholds for decision execution?
What are common anti-patterns in GenAI agent state management?
How do you sandbox agent outputs when interacting with sensitive data?
What are safe task decomposition strategies for autonomous agents?
How would you A/B test two competing agent strategies for task planning?
Section 80: Scaling GenAI Across Enterprise (10 Questions)
How do you build a GenAI capability map across departments?
What are key enablers for GenAI adoption in sales, marketing, HR, and ops?
How do you ensure security and compliance when democratizing GenAI access?
What’s your approach to internal LLM platform-as-a-service (PaaS) rollouts?
How do you prioritize GenAI use cases by ROI and implementation risk?
What organizational structures support GenAI centers of excellence?
How do you manage prompt governance in large teams using the same LLM?
How do you train cross-functional teams on safe GenAI development practices?
What’s your playbook for moving from prototype to production GenAI tools?
How do you ensure GenAI experimentation doesn’t fragment product architecture?
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