+ Add

GenAI & LLM Engineering Objective Questions and Answers

Test your skills with GenAI and LLM engineering objective questions with answers and detailed explanations. Covers transformers, prompt engineering, RAG, embeddings, fine-tuning, inference optimization, vector databases, agents, evaluation, and production best practices.

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.

Practice GenAI & LLM Engineering MCQs with Detailed Explanations

Answer at least 12 questions to submit.

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
Answered: 0 / 15

Other Objective Questions