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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.

16.
What is the primary benefit of using caching in LLM APIs?
Medium
17.
Which approach is best for grounding LLM responses in real-time data?
Medium
18.
What is a major challenge when deploying LLM agents in production?
Hard
19.
Which evaluation method best measures factual consistency of LLM outputs?
Hard
20.
What does top-k sampling control?
Medium
21.
Which technique helps align LLM outputs with human preferences?
Medium
22.
What is the main benefit of using streaming responses in chat applications?
Medium
23.
Which risk increases when using very large overlapping chunks in RAG?
Hard
24.
What is the main purpose of prompt templates?
Medium
25.
Which failure is most likely if the retriever returns irrelevant documents?
Hard
26.
What is the role of temperature = 0 in decoding?
Easy
27.
Which component is critical for semantic search at scale?
Medium
28.
What is the key advantage of hybrid search (keyword + vector)?
Hard
29.
Which is a common cause of prompt overfitting in production?
Hard
30.
What is the main goal of model distillation for LLMs?
Medium
Answered: 0 / 15

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