LLM Basics & Interview Prep: Essential Concepts Quiz — 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: Identifying Generative AI

   Which of the following best describes a key difference between generative AI and traditional discriminative AI?

   • A. Generative AI creates new content, while discriminative AI classifies existing data.
   • B. Generative AI sorts data into categories, while discriminative AI invents data.
   • C. Generative AI uses only labeled data, while discriminative AI uses unlabeled data.
   • D. Generative AI can only translate text, discriminative AI can only summarize.
   • E. Generative AI deletes irrelevant information, discriminative AI stores it.
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   Correct answer: A. Generative AI creates new content, while discriminative AI classifies existing data.

2. Question 2: Tokens in Language Models

   In the context of large language models, what is a 'token'?

   • A. A mathematical equation performed by the model
   • B. A unit of text, such as a word or part of a word, used as input or output
   • C. A binary code required for model authentication
   • D. An image used for training the model
   • E. A predefined answer stored in the database
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   Correct answer: B. A unit of text, such as a word or part of a word, used as input or output

3. Question 3: Understanding Prompt Engineering

   What is one main goal of prompt engineering in large language models?

   • A. Formatting input in a way that guides the model toward producing the desired output
   • B. Coding new neural networks from scratch
   • C. Training the model with audio samples
   • D. Compressing the model's size
   • E. Deleting incorrect responses automatically
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   Correct answer: A. Formatting input in a way that guides the model toward producing the desired output

4. Question 4: Retrieval Augmented Generation (RAG)

   How does Retrieval Augmented Generation (RAG) improve large language model outputs?

   • A. By randomly generating answers based on training data
   • B. By retrieving relevant information from external data sources to support responses
   • C. By compressing the text outputs
   • D. By removing all ambiguous words from answers
   • E. By prioritizing speed over accuracy
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   Correct answer: B. By retrieving relevant information from external data sources to support responses

5. Question 5: Purpose of Chunking

   Why do we use chunking strategies in preparing data for large language models?

   • A. To make data easier for hardware to access by splitting it into meaningful segments
   • B. To translate data into multiple languages automatically
   • C. To increase the randomness in outputs
   • D. To remove all small words from the dataset
   • E. To convert text to images for better understanding
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   Correct answer: A. To make data easier for hardware to access by splitting it into meaningful segments

6. Question 6: Vector Embeddings Explained

   What is a vector embedding in the context of large language models?

   • A. A fixed-length numeric representation of text capturing its meaning
   • B. A picture used to summarize a sentence
   • C. A database error code
   • D. A direct copy of the original document
   • E. An automatically generated graph of outputs
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   Correct answer: A. A fixed-length numeric representation of text capturing its meaning

7. Question 7: Vector Database Basics

   What is one key difference between a vector database and a traditional database?

   • A. Vector databases store data as high-dimensional vectors for similarity search, while traditional databases use structured tables
   • B. Vector databases only store images, traditional databases only store text
   • C. Vector databases are less secure than traditional databases
   • D. Traditional databases operate offline, vector databases require the internet
   • E. Vector databases cannot be queried by users
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   Correct answer: A. Vector databases store data as high-dimensional vectors for similarity search, while traditional databases use structured tables

8. Question 8: Temperature Parameter Meaning

   In large language models, what does the temperature parameter control?

   • A. The randomness or creativity of the generated output
   • B. The server's operating temperature
   • C. The number of tokens per answer
   • D. The model's training speed
   • E. The amount of memory allocated
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   Correct answer: A. The randomness or creativity of the generated output

9. Question 9: Stopping Criteria in LLMs

   Which of the following is an example of a stopping criteria for a large language model?

   • A. Setting a specific stop sequence, such as '\n\n', to indicate when the model should halt generation
   • B. Instructing the model to use uppercase only
   • C. Ordering results alphabetically
   • D. Requesting images instead of text outputs
   • E. Forcing the model to repeat the same sentence
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   Correct answer: A. Setting a specific stop sequence, such as '\n\n', to indicate when the model should halt generation

10. Question 10: Purpose of Fine-Tuning

    Why might someone fine-tune a large language model on their own data?

    • A. To adapt the model so it performs better on specific tasks or domains relevant to the user
    • B. To increase the number of hidden layers in the model
    • C. To make the model train faster on new datasets only
    • D. To transform text into audio output
    • E. To disable all randomization in answers
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    Correct answer: A. To adapt the model so it performs better on specific tasks or domains relevant to the user