GenAI & LLM Engineering Objective Questions and Answers - Set 3

This GenAI & LLM engineering quiz contains carefully curated objective questions with correct answers and clear explanations. It is designed for developers and ML engineers to test your skills in building, optimizing, evaluating, and deploying LLM-powered applications. This is part 3 of the series.

Practice GenAI & LLM Engineering MCQs with Detailed Explanations

Answer at least 12 questions to submit and verify answers.

31 Which scenario benefits most from function calling or tool calling? Medium

32 What is the primary challenge with long-term memory in LLM agents? Hard

33 Which approach best supports multi-turn conversation consistency? Medium

34 What is a major drawback of beam search for open-ended generation? Hard

35 Which factor most influences embedding quality for domain-specific retrieval? Medium

36 What is the main risk of storing raw user prompts for training? Hard

37 Which technique helps reduce token usage in long conversations? Medium

38 What is the main benefit of using evaluation harnesses for LLMs? Hard

39 Which method best reduces cost for high-volume LLM usage? Medium

40 What is the key purpose of guardrails in LLM applications? Medium

41 Which factor most affects vector search accuracy at scale? Hard

42 What is the main trade-off of aggressive vector compression? Hard

43 Which approach improves reliability of LLM outputs in critical workflows? Hard

44 What is the main risk of tool hallucination in agents? Hard

45 Which technique best supports multilingual retrieval in RAG? Medium