Large Language Model Interview Questions Quiz
Top 30 curated Interview Questions Quiz for Large Language Models asked in Fortune 500 organizations.
We are presenting the top 30 Interview Quiz questions (Usually a first round in many Fortune 500 organizations) to test your knowledge of LLM.
We’ve curated 30 thought-provoking questions asked in top Fortune 500 companies.
- Prompt engineering
- LLMs
- SFT (Supervise Fine-Tuning)
- Deployment
- Vector DB
- Embedding
- Agents
- Evaluation
- Prompt Hacking
Spend 30 mins to note your answers, you will find detailed explanations and correct answers at the end of the blog.
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Question 1
Scenario: Building an AI Chatbot for a Family-Friendly Game
You work for a game development company that is creating a family-friendly virtual world. As part of this project, you are responsible for developing an AI chatbot that interacts with players. The chatbot should be fun, engaging, and helpful, but it must also adhere to strict guidelines to avoid offensive or inappropriate language.
As a developer working on the game’s chatbot, what are reasonable techniques you will use to ensure that the chatbot’s responses do not contain offensive words or content? [Multiple correct answers]
A. In your instructions to the LLM, include one telling it to remove all offensive words from the output.
B. Ask the model to think step-by-step to come to a solution.
C. Train a new model on a dataset that does not contain any offensive words.
D. Use a second model to clean the main LLM’s output, removing any offensive words.
Question 2
What is a potential issue when a Large Language Model (LLM) is trained with a few-shot learning approach?
A. The LLM may not understand the examples given in the prompt.
B. The LLM may overfit the few-shot examples.
C. The LLM may not generalize well if the examples are not diverse.
D. All of the above.
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Question 3
What is a recommended strategy when refining prompts for a model?
A. Start with a vague prompt and add more words if performance is inconsistent.
B. Start with as descriptive a prompt as possible and work backwards, removing words if performance is consistent.
C. Always use a single, simple prompt without any modifications.
D. None of the above.
Question 4
Scenario: Improving a Chatbot’s Reasoning Abilities
You are part of a team developing an advanced chatbot that interacts with users across various domains. Despite experimenting with Chain-of-Thought (CoT) prompting, the chatbot’s reasoning abilities have not significantly improved. The team is now exploring alternative strategies to enhance its performance.
As a member of the development team, what alternative strategies would you recommend to enhance the reasoning abilities of the chatbot, considering that CoT prompting did not yield the desired results?
A. Increase the model’s token generation speed.
B. Use a self-consistency strategy that samples a diverse set of reasoning paths and selects the most consistent answer.
C. Reduce the model’s temperature to 0.
D. Use a majority vote strategy without sampling multiple outputs.
Question 5
What are some factors that influence the optimal chunk size? [Multiple correct answers]
A. Data distribution
B. latency requirement
C. embedding model size
D. user query
E. nature of content
F. LLM token limit
Question 6
Scenario: Ensuring Translation System Integrity
You are leading a team tasked with developing a robust machine translation system using a Large Language Model (LLM). The goal is to accurately translate text between languages while preventing any unintended deviations from the primary purpose. However, there is a concern about “goal hijacking,” where the LLM might produce translations that stray from the intended meaning.
As the team lead, what strategies would you implement to protect against ‘goal hijacking’ in your machine translation system, ensuring that translations remain faithful to their intended purpose?
A. Filter out specific words and phrases from user input and output.
B. Add a delimiter to clearly separate user input from system instructions.
C. Include an instruction within the prompt reminding the LLM of its translation goal.
D. Sandwich the user input between two prompts reiterating the translation purpose.
E. Use XML tagging to mark user input and escape special characters.
Question 7
Why do we use dense, rather than sparse, embedding vectors for semantic search? [Multiple correct answers]
A. Dense embedding vectors can encode the context of the words
B. Dense embedding vectors capture semantic relationships better
C. Dense embedding vectors are quicker to compute
D. Dense embedding vectors take up less storage space
Question 8
What metrics are involved in evaluating an embedding model? [Multiple correct answers]
A. Hit rate
B. Mean Squared Error
C. Mean Reciprocal Rank (MRR)
D. Recall@k
E. Normalized Discounted Cumulative Gain (NDCG)
Question 9
Which one of the following is NOT an approximate nearest neighbour search algorithm?
A. Facebook AI Similarity Search (FAISS)
B. Hierarchical Navigable Small Worlds (HNSW)
C. K-nearest neighbours (KNN)
D. Locality-Sensitive Hashing (LSH)
Question 10
You need to have a vector store for all your text use cases?
A. True
B. False
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Question 11
Scenario: Enhancing a Legal Document Retrieval System
You are part of a legal tech startup that aims to build an efficient document retrieval system for legal professionals. The system needs to retrieve relevant legal documents based on complex semantic queries. However, the team is concerned about the effectiveness of certain vector search strategies.
Considering the legal document retrieval system, which vector search strategy would you expect to be less effective when dealing with fine-grained semantic nuances in legal texts?
A. Tree-based ANN (like ANNOY)
B. K-Nearest Neighbor
C. Clustering-based ANN (like FAISS)
D. Vector compression-based ANN (like ScaNN)
Question 12
When would you be MOST likely to prefer a Cross-encoder model over a Bi-encoder?
A. When fast retrieval of documents from a massive dataset is essential.
B. When the task requires understanding the specific context and relationship between query and document.
C. When computational resources and speed are limited, and smaller datasets are handled.
D. When interpretability of the model’s reasoning is crucial for user understanding.
Question 13
LLMs perform optimally when relevant context is available in which positions within the input?
A. Only at the beginning of the context
B. Only at the end of the context.
C. Both at the beginning and end of the context. (U-shaped curve)
D. Randomly scattered throughout the context.
Question 14
What is the purpose of nucleus sampling in language models?
A. It dynamically sets the size of the shortlist of tokens by selecting the top tokens whose sum of likelihoods does not exceed a certain value.
B. It always chooses the highest probable word.
C. It selects from a shortlist of the top K tokens.
D. It chooses the least probable word to ensure diversity in the output.
Question 15
What do language models do?
A. Calculate the probability distribution over each word in the entire vocabulary.
B. Convert the input to numbers and calculate the weighted average of the input tokens.
C. Shuffles the input and produces an output that is a linear combination of the input tokens and a randomized vector.
D. Uses a Gaussian distribution to place each token in the input as close as possible to a predefined mean and use the standard deviation to generate a new word.
Question 16
What is order of execution when you set both top-k and top-p together.
A. Top K acts before Top P
B. Top P acts before Top K
C. Top K does not matter
D. Top P does not matter
Question 17
Is LLM inference memory-io bound or compute bound ?
A. memory-io bound
B. compute bound
Question 18
To create an unbiased model, it’s sufficient to keep collecting more data and train bigger models on it.
A. True
B. False
Question 19
Which is the correct sequential workflow of retrieval-augmented generation?
A. User submits query -> Language model converts query to embeddings -> Search for relevant documents -> Pass context to language model -> Language model outputs responses
B. Search for relevant documents -> User submits query -> Language model converts query to embeddings -> Pass context to language model -> Language model outputs responses
C. Search for relevant documents -> Pass context to language model -> User submits query-> Language model converts query to embeddings-> Language model outputs responses
D. User submits query-> Search for relevant documents-> Language model converts query to embeddings-> Pass context to language model-> Language model outputs response
Question 20
Which statement is TRUE about the key differences between QLoRA and LoRA in fine-tuning large language models (LLMs)?
A. QLoRA only uses pre-trained models, while LoRA can train from scratch.
B. QLoRA reduces the number of trainable parameters, while LoRA increases them.
C. QLoRA uses 4-bit quantization for higher memory efficiency, while LoRA uses 8-bit.
D. QLoRA is less effective than LoRA in fine-tuning tasks.
Question 21
Which of the following is NOT true about evaluating hallucination?
A. There can be subjective nuances to evaluate if particular statements are toxic or factual or not.
B. Evaluating hallucination can be straightforward when we use mathematical equations to quantify it.
C. When we use another model to evaluate hallucination, the model itself can be flawed as well.
D. Many outputs require manual human verification.
Question 22
Which of the following model issues result in hallucination? [Multiple correct answers]
A. Encoder learns wrong correlations between parts of training data
B. Decoder attends to the wrong part of the input source
C. Model generates output based on its own self-generated sequence
D. The model learns from the provided input
Question 23
What is the library, DeepSpeed, most efficient for in terms of cluster configuration?
A. Single-node CPU clusters.
B. Single-node GPU clusters.
C. Multiple-node CPU clusters.
D. Multiple-node and multiple-GPU clusters.
Question 24
Which of the following statements is TRUE about the approach used in “LLM.int8()” to maintain performance accuracy during quantization?
A. It completely ignores outlier values during matrix multiplication.
B. It quantizes all values to 8-bit integers, regardless of their magnitude.
C. It uses a separate normalization constant for each inner product in the matrix multiplication.
D. It performs mixed-precision calculations, using 16-bit for outliers and 8-bit for non-outliers.
Question 25
What is the memory requirement for storing the weights of a 1 billion parameter LLM in full precision?
A. 4 MB
B. 40 GB
C. 4 GB
D. 32 GB
Question 26
What is the memory requirement to fine-tune 13 billion parameter model in full precision?
A. 312 GB
B. 284 GB
C. 70 GB
D. 128 GB
Question 27
Why is perplexity important to consider above and beyond accuracy?
A. Perplexity and accuracy are the same thing.
B. Perplexity is a measure of how confident the model was with predicting the next token, if a model has low perplexity and high accuracy, that is the best state.
C. Accuracy is more important than Perplexity since we really only need to know if the answer is correct or not.
D. Perplexity is a measure of how confident the model was with predicting the next token, if a model has high perplexity and low accuracy, that is the best state.
Question 28
What are two task-specific evaluation metrics? [Multiple correct answers]
A. ROUGE — for summarization tasks
B. ROSE — for question/answering
C. VERDE — for translation and summarization tasks
D. BLEU — for translation tasks
Question 29
What are the steps of the ReAct framework for LLM Agents?
A. Thought–Observation–Action
B. Thought–Action–Observation
C. Stop–Drop–Roll
D. Act–React–Readapt
Question 30
What is the MAIN advantage of ReAct prompting over “chain-of-thought” reasoning?
A. It requires less computational power.
B. It allows interaction with external knowledge sources.
C. It generates outputs in a structured format.
D. It is easier to implement without additional tools.
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