Large Language Models & Generative AI: Easy Interview Basics Quiz

Explore 10 beginner-friendly questions about Large Language Models, Generative AI, and related foundational technologies to prepare for interviews in this fast-growing field.

1. Definition of LLM

   What is a Large Language Model (LLM)?

   1. A small rule-based system for basic language tasks
   2. A deep learning model trained on massive text data for understanding and generating human-like text
   3. A database storing language rules

   Explanation: LLMs are deep learning models trained on extensive text datasets and use neural networks to process and generate text. The rule-based system is outdated and limited, while a database is not a model and cannot generate text.

2. Main Architecture Used

   Which architecture do most Large Language Models use?

   1. Decision Tree
   2. Transformer
   3. Recurrent Neural Network (RNN)

   Explanation: Transformers are the backbone architecture for LLMs due to their self-attention mechanism and scalability. RNNs were used previously but lack the capability for large-scale learning. Decision Trees do not handle language modeling effectively.

3. Key Transformer Component

   What is a key component of the Transformer architecture?

   1. Sparse matrix multiplication
   2. Image convolution layers
   3. Self-attention mechanism

   Explanation: Self-attention allows Transformers to relate information from any part of the input sequence, which is vital for language tasks. Image convolution layers are used in vision, and sparse matrices are not a defining feature.

4. Parameter Scale in LLMs

   How many parameters do LLMs typically have?

   1. Fifty thousand
   2. Billions or trillions
   3. Hundreds

   Explanation: Modern LLMs are built with billions or even trillions of parameters for powerful capabilities. Hundreds or tens of thousands are typical of far smaller models and are insufficient for LLM performance.

5. LLM Core Task Example

   Which task is well-suited for Large Language Models?

   1. Image classification
   2. Signal denoising
   3. Text summarization

   Explanation: LLMs are designed for natural language tasks like summarization. Image classification and signal denoising are tasks for other types of models and not the primary domain of LLMs.

6. Learning Approaches

   Which learning approach can LLMs use for various NLP tasks?

   1. Reinforcement learning with images
   2. Few-shot or zero-shot learning
   3. Supervised image labeling

   Explanation: LLMs can perform new tasks with very few or no examples using few-shot and zero-shot learning. The other options focus on image-based tasks, not NLP.

7. Text Generation Category

   Text generation by an LLM falls under what type of AI task?

   1. Clustering
   2. Natural Language Processing (NLP)
   3. Genomic sequencing

   Explanation: Generating human-like text is a core NLP activity. Genomic sequencing and clustering are unrelated fields, making them unsuitable as answers.

8. Main Use Case for Generative AI

   What is a primary application of Generative AI models?

   1. Creating new, human-like content
   2. Counting the number of vowels in words
   3. Sorting numbers in order

   Explanation: Generative AI excels at constructing new content, such as text, images, or music. The other options are basic operations not specific to generative models.

9. RAG Technique

   What does RAG (Retrieval-Augmented Generation) do in AI?

   1. Retrieves relevant information to improve generated text
   2. Detects malware in binary files
   3. Generates images from noise

   Explanation: RAG adds external information to enhance the model's response. The other options refer to different applications outside the scope of RAG.

10. Multimodal AI Capability

    Which describes a key feature of Multimodal AI?

    1. Processing and combining data from multiple formats, like text and images
    2. Focusing only on tabular data
    3. Ignoring sequence relationships

    Explanation: Multimodal AI integrates multiple data types for richer analysis. The other answers ignore this integration and represent limited or incorrect capabilities.