Curated · Multiple Choice
Top 15 Multiple Choice Interview Questions
Here are the top 15 AI interview questions in Multiple Choice format, sorted by community quality. Each entry links to a detailed answer with explanations, hints, and source-grade follow-ups. See all in this format →
Sorted by community quality · refreshed as new questions are published.
- 01What is RLHF, and why is it used after pretraining?Multiple ChoiceEasy·Qual 4.7AnthropicOpenAI
- 02What does LoRA do, and why is it popular for fine-tuning?Multiple ChoiceMedium·Qual 4.7
- 03In LLM serving, what is the primary driver of end to end latency for a generation request?Multiple ChoiceMedium·Qual 4.7MetaNVIDIA
- 04Which metric best measures whether a RAG answer is grounded in the retrieved context?Multiple ChoiceMedium·Qual 4.7
- 05What does RAG primarily help with in LLM-based applications?Multiple ChoiceEasy·Qual 4.6
- 06Why combine BM25 with dense embeddings for retrieval?Multiple ChoiceEasy·Qual 4.6
- 07What is the Model Context Protocol (MCP) and what problem does it solve?Multiple ChoiceEasy·Qual 4.6
- 08What distinguishes an AI agent from a plain LLM call?Multiple ChoiceEasy·Qual 4.5
- 09Which components make up a standard transformer block?Multiple ChoiceEasy·Qual 4.5Mistral AI
- 10What is the primary purpose of embeddings in modern NLP?Multiple ChoiceEasy·Qual 4.3
- 11In LoRA, where in the forward pass does `lora_dropout` apply its mask?Multiple ChoiceEasy·Qual 4.0
- 12At which step in the attention pipeline does the mask actually take effect?Multiple ChoiceEasy·Qual 4.0
- 13Why do modern LLMs use subword tokenization instead of word-level or character-level approaches?Multiple ChoiceEasy·Qual 4.0
- 14Identify the context vector inside scaled dot-product attention and what produces it.Multiple ChoiceEasy·Qual 4.0
- 15Which loss function does supervised fine-tuning actually minimize?Multiple ChoiceEasy·Qual 4.0
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