Generative AI Interview Simulator - Practice Questions with Answers and Scoring

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Top Generative AI Interview Questions for Freshers and Experienced

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1 What is Generative AI and how is it different from traditional machine learning?

easy basicsml
Answer
Generative AI creates new data similar to training data, unlike traditional ML which predicts labels. Key concept: Data generation vs classification. Example: GPT generating text vs classifier predicting sentiment.
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2 Explain how Large Language Models (LLMs) work at a high level.

medium llmtransformer
Answer
LLMs use transformer architecture to predict next tokens based on context. Key concept: Self-attention mechanism. Example: Predicting next word in a sentence.
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3 What is a transformer architecture and why is it important?

medium transformerarchitecture
Answer
Transformers process sequences using self-attention instead of RNNs. Key concept: Parallel processing and context awareness. Example: BERT, GPT models.
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4 What is tokenization in NLP models?

easy tokensnlp
Answer
Tokenization splits text into smaller units (tokens). Key concept: Model processes tokens, not raw text. Example: 'hello world' → ['hello', 'world'].
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5 Explain the concept of embeddings in Generative AI.

medium embeddingsvector
Answer
Embeddings convert text into numerical vectors. Key concept: Semantic similarity. Example: Similar words have closer vectors.
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6 What is fine-tuning in Generative AI?

medium finetuningtraining
Answer
Fine-tuning adapts a pre-trained model to a specific task. Key concept: Transfer learning. Example: Fine-tuning GPT for legal text.
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7 What is prompt engineering and why is it important?

medium promptdesign
Answer
Designing inputs to guide model outputs. Key concept: Input strongly influences output. Example: Structured prompts improve accuracy.
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8 Explain temperature parameter in text generation.

easy parametersllm
Answer
Controls randomness of output. Key concept: Higher = more creative, lower = deterministic. Example: temperature 0.2 for factual answers.
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9 What is top-k and top-p sampling?

hard samplinggeneration
Answer
Methods to control token selection. Key concept: Limit candidate tokens. Top-k picks k tokens; top-p uses probability threshold.
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10 What is hallucination in Generative AI?

medium hallucinationllm
Answer
When model generates incorrect or fabricated information. Key concept: Lack of grounding. Example: Fake facts in answers.
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11 How can hallucinations be reduced?

hard hallucinationrag
Answer
Use RAG, better prompts, and validation. Key concept: Ground responses with data. Example: retrieve documents before answering.
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12 What is Retrieval-Augmented Generation (RAG)?

medium ragretrieval
Answer
Combines retrieval system with generation model. Key concept: External knowledge integration. Example: search + generate answer.
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13 Explain the role of vector databases in Generative AI.

medium vector-dbembeddings
Answer
Stores embeddings for similarity search. Key concept: Fast retrieval of relevant data. Example: Pinecone, FAISS.
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14 What is few-shot prompting?

medium promptfew-shot
Answer
Providing examples in prompt. Key concept: Guides model behavior. Example: showing sample Q&A.
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15 What is zero-shot learning?

medium zero-shotlearning
Answer
Model performs task without examples. Key concept: Generalization ability. Example: classify unseen labels.
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16 What is the difference between fine-tuning and prompt engineering?

medium finetuningprompt
Answer
Fine-tuning updates model weights; prompt engineering changes input. Key concept: Training vs inference control. Prompt is cheaper and faster.
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17 How does self-attention work in transformers?

hard attentiontransformer
Answer
Each token attends to others in sequence. Key concept: Context-aware representation. Example: word meaning depends on sentence.
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18 What is positional encoding?

hard transformerencoding
Answer
Adds position info to tokens. Key concept: Transformers lack sequence order. Example: sine/cosine encoding.
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19 What are diffusion models?

hard diffusionmodels
Answer
Generate data by reversing noise process. Key concept: Stepwise denoising. Used in image generation.
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20 What are GANs and how do they work?

medium ganmodels
Answer
Generator and discriminator compete. Key concept: Adversarial training. Example: realistic image generation.
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21 What is model overfitting in generative models?

medium overfittingtraining
Answer
Model memorizes training data. Key concept: Poor generalization. Example: repeating same outputs.
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22 How do you evaluate Generative AI models?

medium evaluationmetrics
Answer
Use BLEU, ROUGE, human evaluation. Key concept: Quality measurement. Depends on task.
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23 What is prompt injection attack?

hard securityprompt
Answer
Malicious input manipulates model behavior. Key concept: Security risk. Example: overriding instructions.
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24 How can prompt injection be mitigated?

hard securitymitigation
Answer
Sanitize inputs and validate outputs. Key concept: Input filtering. Use guardrails.
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25 What is context window limitation?

medium tokenscontext
Answer
Max tokens model can process. Key concept: Input + output limit. Affects prompt size.
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26 How do you handle long documents in LLMs?

medium chunkingprocessing
Answer
Use chunking and summarization. Key concept: Split and process. Example: sliding window.
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27 What is chain-of-thought prompting?

medium cotreasoning
Answer
Encourages step-by-step reasoning. Key concept: Improves reasoning tasks. Example: show intermediate steps.
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28 What is tool usage in Generative AI?

hard toolsintegration
Answer
Models call external tools/APIs. Key concept: Extend capabilities. Example: calculator API.
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29 Explain multi-modal Generative AI.

medium multimodalai
Answer
Handles text, image, audio together. Key concept: Cross-modal understanding. Example: image captioning.
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30 What is latency in Generative AI systems?

easy latencyperformance
Answer
Time taken to generate output. Key concept: Performance metric. Affected by model size.
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31 How can you reduce latency in LLM applications?

hard latencyoptimization
Answer
Use caching, smaller models, streaming. Key concept: Optimization. Example: response streaming.
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32 What is model quantization?

hard quantizationoptimization
Answer
Reduce model precision for efficiency. Key concept: Lower memory usage. Example: 32-bit to 8-bit.
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33 What is distillation in AI models?

hard distillationmodels
Answer
Train smaller model from larger one. Key concept: Knowledge transfer. Example: student-teacher models.
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34 What is beam search?

hard beam-searchgeneration
Answer
Keeps top sequences during generation. Key concept: Balanced exploration. Used in translation tasks.
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35 What is temperature vs top-p tradeoff?

medium samplingparameters
Answer
Temperature controls randomness; top-p controls diversity. Key concept: Output tuning. Used together for balance.
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36 What is alignment in Generative AI?

medium alignmentethics
Answer
Ensuring model follows human values. Key concept: Safety and ethics. Example: RLHF.
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37 Explain RLHF (Reinforcement Learning from Human Feedback).

hard rlhftraining
Answer
Uses human feedback to fine-tune models. Key concept: Preference learning. Improves output quality.
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38 What is prompt chaining?

medium promptworkflow
Answer
Multiple prompts linked sequentially. Key concept: Workflow design. Output feeds next step.
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39 How do you debug poor model responses?

hard debuggingllm
Answer
Analyze prompts, logs, and outputs. Key concept: Iterative tuning. Adjust context and parameters.
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40 What is caching in Generative AI systems?

easy cacheperformance
Answer
Store responses to reuse. Key concept: Reduce cost and latency. Example: prompt-response cache.
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41 What is semantic similarity?

easy semanticvector
Answer
Measure of meaning closeness. Key concept: Vector distance. Used in search.
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42 What are guardrails in Generative AI?

medium guardrailssafety
Answer
Constraints on outputs. Key concept: Safety and compliance. Example: content filtering.
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43 How do you design scalable Generative AI systems?

hard scalingarchitecture
Answer
Use distributed systems and async calls. Key concept: Horizontal scaling. Example: microservices.
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44 What is batch inference?

medium batchinference
Answer
Process multiple inputs together. Key concept: Efficiency. Used in offline processing.
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45 What is online inference?

medium inferencerealtime
Answer
Real-time prediction. Key concept: Low latency. Used in chat apps.
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0 / 0 answered