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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.

46.
What is a key challenge in evaluating LLMs for reasoning tasks?
Hard
47.
Which method improves robustness against adversarial prompts?
Hard
48.
What is the main benefit of asynchronous inference in LLM APIs?
Medium
49.
Which practice best supports continuous improvement of prompts?
Medium
50.
What is the main risk of exposing internal prompts to end users?
Hard
51.
Which approach improves determinism in structured outputs?
Medium
52.
What is the main drawback of relying solely on fine-tuning instead of RAG?
Medium
53.
Which strategy best supports cost control in peak traffic scenarios?
Hard
54.
What is the key challenge of multi-agent LLM systems?
Hard
55.
Which technique improves grounding when retrieved context is noisy?
Hard
56.
What is the main benefit of token streaming to frontend apps?
Medium
57.
Which failure mode occurs when the LLM follows retrieved context even if it is incorrect?
Hard
58.
Which method helps ensure structured JSON outputs from LLMs?
Medium
59.
What is the main operational risk of long-running agent workflows?
Hard
60.
Which approach best supports safe deployment of autonomous LLM agents?
Hard
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