Essential LLM Interview Questions for Data Scientists Quiz

1. Understanding LLMs

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

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

2. Transformer Structure

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

   1. Single-layer perceptron
   2. Tree-to-sequence mapping
   3. Random forest splitting
   4. Multi-head attention
   5. Convolutional pooling

3. Training Processes

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

   1. Fine-tuning always results in a larger model size.
   2. Pretraining uses only labeled data; fine-tuning uses unlabeled data.
   3. Pretraining trains on images; fine-tuning trains on audio.
   4. Pretraining learns general patterns; fine-tuning adapts to specific tasks.
   5. Pretraining occurs after fine-tuning has finished.

4. Tokenization Basics

   Why is tokenization important in working with LLMs?

   1. It sorts words alphabetically before training.
   2. It adds random noise to improve results.
   3. It splits text into smaller units for model processing.
   4. It translates text into other languages automatically.
   5. It filters out numbers from the dataset.

5. Working with Context

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

   1. The order of tokenization in the dataset.
   2. The total training time for the model.
   3. A collection of random sentences fed to the model.
   4. The screen size required for model output.
   5. The maximum number of tokens the model can process at once.

6. Masked Language Modeling

   How does a masked language model like BERT typically operate?

   1. By predicting words that have been masked in a sentence using surrounding context.
   2. By generating new stories from scratch.
   3. By replacing all punctuation with tokens.
   4. By translating masked words into different languages.
   5. By removing all stopwords from text.

7. Model Comparisons

   Which feature distinguishes BERT from GPT models?

   1. BERT cannot be fine-tuned; GPT can.
   2. GPT trains solely on labeled data; BERT does not.
   3. Both use decision tree architectures.
   4. BERT is bidirectional, focusing on understanding; GPT is autoregressive, focusing on generation.
   5. BERT only processes images; GPT only processes audio.

8. Understanding Attention

   In a transformer, what do attention weights represent?

   1. How much focus the model gives to each token when processing input.
   2. The font size of input text.
   3. The physical memory used for training.
   4. The size of the training dataset.
   5. The total number of layers in the model.

9. Deployment Challenges

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

   1. Complete absence of bias in outputs.
   2. High resource consumption and latency issues.
   3. Unlimited context window size for tokens.
   4. Instantaneous real-time performance without errors.
   5. Zero risk of data privacy concerns.

10. Improving Model Safety

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

    1. Doubling the batch size each epoch
    2. Using grayscale images during training
    3. Reinforcement learning with human feedback (RLHF)
    4. Lowering the model’s parameter count
    5. Randomly removing output tokens