Essential GenAI Interview Fundamentals Quiz

Explore the core ideas behind generative AI interviews, including language models, RAG pipelines, prompt engineering, deployment strategies, and evaluation techniques—all focused on real-world scenarios and hands-on knowledge.

1. Understanding Language Models

   What is a language model primarily designed to do in the context of GenAI?

   1. Store and retrieve user data securely
   2. Convert images to text automatically
   3. Generate and predict text based on input sequences

   Explanation: A language model's core function is to generate and predict text, helping systems understand and produce language. It does not focus on data storage or retrieval, nor is its main purpose converting images to text (which relates to OCR or vision models).

2. Transformers and Attention

   How does the attention mechanism in a transformer benefit language models?

   1. It allows the model to focus on relevant words when processing input
   2. It translates text from one language to another
   3. It compresses data for efficient storage

   Explanation: Attention lets the model weigh the importance of different words, improving understanding. Compressing data and direct translation aren't the main purposes of attention, though transformers can be part of translation pipelines when trained for it.

3. Prompt Engineering

   Why is prompt engineering considered crucial for GenAI applications?

   1. It makes the codebase smaller
   2. It helps guide model outputs toward desired results
   3. It speeds up hardware computations

   Explanation: Prompt engineering shapes how models respond and extract information. It doesn't alter hardware speed or reduce code size, but rather improves output control and reliability.

4. Retrieval-Augmented Generation (RAG)

   What is the main purpose of a RAG pipeline in GenAI?

   1. Encrypt communications between users
   2. Combine retrieval from external data sources with language generation
   3. Fine-tune a model using image data

   Explanation: RAG pipelines merge document retrieval with generation, enabling more accurate and context-aware outputs. Encryption and image data fine-tuning are not their focus.

5. Tokenization Impact

   How does tokenization affect the performance and cost of GenAI models?

   1. It speeds up data transfer rates
   2. It improves network connectivity
   3. It determines how text is split, impacting efficiency and computation needed

   Explanation: Tokenization impacts how models process input and output, affecting cost and speed. Network and data transfer are unrelated to tokenization in this context.

6. In-Context Learning

   What describes in-context learning in GPT models?

   1. The model requires fine-tuning on every new task
   2. The model relies only on labeled datasets
   3. The model can generalize tasks by using examples in the prompt without explicit retraining

   Explanation: In-context learning enables models to adapt using prompt-based examples, without needing full retraining or exclusive reliance on labeled data.

7. Optimizing RAG Search

   If your RAG app returns irrelevant documents, which method can help fix it?

   1. Switching to a different programming language
   2. Reducing API call frequency
   3. Improving your chunking strategy or relevance scoring

   Explanation: Enhancing chunk size or relevance scoring can boost RAG accuracy. Changing programming language or API call rate doesn't directly impact retrieval relevance.

8. Caching in GenAI Apps

   Why is caching LLM outputs with tools like Redis beneficial for GenAI deployments?

   1. It trains the model faster
   2. It encrypts the model's weights
   3. It reduces repeated computation and improves response speed

   Explanation: Caching avoids duplicate processing and speeds up responses. It doesn't handle encryption or training speed, which are unrelated in this context.

9. Handling Hallucinations

   What is one way to detect and fix hallucinations in LLM outputs?

   1. Limit model input to only numbers
   2. Increase model temperature indefinitely
   3. Manually review and adjust prompts or retrieve grounded facts from external data

   Explanation: Reviewing and refining prompts or leveraging external knowledge helps minimize hallucinations. High temperature increases randomness, and inputting only numbers restricts model utility.

10. Evaluating Classification Models

    Which technique is appropriate for evaluating a multi-class classification model?

    1. Measuring document length
    2. Calculating accuracy across all classes
    3. Counting the number of tokens

    Explanation: Multi-class models are commonly assessed by accuracy metrics. Token counts and document length aren't suitable for evaluating prediction correctness.