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Prompt Engineering Objective Questions and Answers - Set 2
This prompt engineering quiz contains carefully curated objective questions with correct answers and clear explanations. It is designed for developers, AI practitioners, and interview preparation, covering modern prompting strategies, LLM behavior, optimization techniques, and safety practices. This is part 2 of the series.
Practice Prompt Engineering MCQs with Detailed Explanations
Answer at least 12 questions to submit and verify answers.
16 What is iterative prompting? Medium
Correct answer:
Refining prompts based on outputs
Iterative prompting improves results by refining prompts step-by-step.
17 Which technique improves reasoning in math problems? Medium
Correct answer:
Chain-of-thought
Chain-of-thought helps break down complex reasoning tasks.
18 What is hallucination in LLMs? Medium
Correct answer:
Incorrect but confident output
Hallucination refers to fabricated or incorrect responses.
19 Which improves prompt clarity? Easy
Correct answer:
Specific instructions
Clear and specific instructions produce better results.
20 What is prompt chaining? High
Correct answer:
Combining multiple prompts sequentially
Prompt chaining links multiple prompts to solve complex tasks.
21 Which is best for structured output? Medium
Correct answer:
JSON format instructions
Specifying output format like JSON ensures structured responses.
22 What is output grounding? High
Correct answer:
Linking output to reliable sources
Grounding ensures responses are based on factual or trusted data.
23 Which reduces repetition in output? High
Correct answer:
Frequency penalty
Frequency penalty discourages repeated tokens.
24 What is tokenization? Medium
Correct answer:
Splitting text into tokens
Tokenization breaks text into smaller units for model processing.
25 Which prompt is ambiguous? Medium
Correct answer:
Explain REST
Ambiguous prompts lack context and clarity.
26 What is context window? Medium
Correct answer:
Maximum tokens model can process
Context window defines how much text the model can consider.
27 Which helps avoid prompt injection? High
Correct answer:
Sanitizing user input
Sanitizing input helps mitigate prompt injection attacks.
28 What is few-shot limitation? High
Correct answer:
Limited examples due to token constraints
Few-shot is limited by token size constraints.
29 Which improves determinism? Medium
Correct answer:
Low temperature
Lower temperature produces more deterministic outputs.
30 What is instruction tuning? High
Correct answer:
Fine-tuning models on instructions
Instruction tuning trains models to follow instructions better.