Description
Conversational AI Interview Questions and Answers basic to Advanced, Real-time Scenario-Based and Coding Based
Conversational AI Interview Topics Basic to Advanced
- Fundamentals of Conversational AI — architectures, agent types (chatbot vs voicebot), synchronous vs asynchronous interactions.
- Natural Language Understanding — intent detection, entity recognition, slot filling, utterance classification.
- Automatic Speech Recognition and Text To Speech — ASR models, noise robustness, TTS voices, latency and quality tradeoffs.
- Dialog Management and State Tracking — rule‑based vs learned policies, context propagation, session management.
- Natural Language Generation and Response Planning — template vs neural NLG, controllable generation, safety filters.
- Knowledge Integration and Retrieval — RAG, vector search, knowledge graphs, grounding responses in external data.
- Multimodal and Channel Integration — voice, text, IVR, messaging platforms, multimodal inputs and fallback strategies.
- Evaluation and Metrics — intent accuracy, WER, BLEU/ROUGE, user satisfaction, A/B testing, human evaluation.
- Scalability and Infrastructure — real‑time streaming, batching, autoscaling, latency SLAs, edge vs cloud deployment.
- Security, Privacy, and Compliance — PII handling, consent, encryption, GDPR/region-specific requirements.
- Monitoring, Observability, and Debugging — logging, conversation replay, root‑cause analysis, alerting for regressions.
- Advanced ML Topics — fine‑tuning LLMs, prompt engineering, continual learning, domain adaptation, hallucination mitigation.
- Design, Governance, and Ethics — conversational UX, persona design, bias mitigation, governance and escalation flows.
“Conversational AI combines language understanding with dialog management to create interactive agents.” “Robust systems require careful integration of ASR/TTS, NLU, knowledge retrieval, and monitoring.”
