Essential LLM Interview Questions for Data Scientists — Questions & Answers

This quiz contains 10 questions. Below is a complete reference of all questions, answer choices, and correct answers. You can use this section to review after taking the interactive quiz above.

1. Question 1: Understanding LLMs

   Which statement best describes a Large Language Model (LLM)?

   • A handwritten set of language rules for translation.
   • A simple algorithm for sorting text alphabetically.
   • A small database of vocabulary and grammar rules.
   • A large-scale neural network trained on vast text data to understand and generate human language.
   • A collection of unrelated images and audio files.
   Show correct answer

   Correct answer: A large-scale neural network trained on vast text data to understand and generate human language.

2. Question 2: Transformer Structure

   Which of the following is a key component of the Transformer architecture commonly used in LLMs?

   • Single-layer perceptron
   • Tree-to-sequence mapping
   • Random forest splitting
   • Multi-head attention
   • Convolutional pooling
   Show correct answer

   Correct answer: Multi-head attention

3. Question 3: Training Processes

   What is the main difference between pretraining and fine-tuning in large language models (LLMs)?

   • Fine-tuning always results in a larger model size.
   • Pretraining uses only labeled data; fine-tuning uses unlabeled data.
   • Pretraining trains on images; fine-tuning trains on audio.
   • Pretraining learns general patterns; fine-tuning adapts to specific tasks.
   • Pretraining occurs after fine-tuning has finished.
   Show correct answer

   Correct answer: Pretraining learns general patterns; fine-tuning adapts to specific tasks.

4. Question 4: Tokenization Basics

   Why is tokenization important in working with LLMs?

   • It sorts words alphabetically before training.
   • It adds random noise to improve results.
   • It splits text into smaller units for model processing.
   • It translates text into other languages automatically.
   • It filters out numbers from the dataset.
   Show correct answer

   Correct answer: It splits text into smaller units for model processing.

5. Question 5: Working with Context

   What does 'context window' mean when referring to LLMs such as GPT-3?

   • The order of tokenization in the dataset.
   • The total training time for the model.
   • A collection of random sentences fed to the model.
   • The screen size required for model output.
   • The maximum number of tokens the model can process at once.
   Show correct answer

   Correct answer: The maximum number of tokens the model can process at once.

6. Question 6: Masked Language Modeling

   How does a masked language model like BERT typically operate?

   • By predicting words that have been masked in a sentence using surrounding context.
   • By generating new stories from scratch.
   • By replacing all punctuation with tokens.
   • By translating masked words into different languages.
   • By removing all stopwords from text.
   Show correct answer

   Correct answer: By predicting words that have been masked in a sentence using surrounding context.

7. Question 7: Model Comparisons

   Which feature distinguishes BERT from GPT models?

   • BERT cannot be fine-tuned; GPT can.
   • GPT trains solely on labeled data; BERT does not.
   • Both use decision tree architectures.
   • BERT is bidirectional, focusing on understanding; GPT is autoregressive, focusing on generation.
   • BERT only processes images; GPT only processes audio.
   Show correct answer

   Correct answer: BERT is bidirectional, focusing on understanding; GPT is autoregressive, focusing on generation.

8. Question 8: Understanding Attention

   In a transformer, what do attention weights represent?

   • How much focus the model gives to each token when processing input.
   • The font size of input text.
   • The physical memory used for training.
   • The size of the training dataset.
   • The total number of layers in the model.
   Show correct answer

   Correct answer: How much focus the model gives to each token when processing input.

9. Question 9: Deployment Challenges

   Which is a common limitation of deploying LLMs in production environments?

   • Complete absence of bias in outputs.
   • High resource consumption and latency issues.
   • Unlimited context window size for tokens.
   • Instantaneous real-time performance without errors.
   • Zero risk of data privacy concerns.
   Show correct answer

   Correct answer: High resource consumption and latency issues.

10. Question 10: Improving Model Safety

    Which approach helps reduce the chances of an LLM generating biased or toxic content?

    • Doubling the batch size each epoch
    • Using grayscale images during training
    • Reinforcement learning with human feedback (RLHF)
    • Lowering the model’s parameter count
    • Randomly removing output tokens
    Show correct answer

    Correct answer: Reinforcement learning with human feedback (RLHF)