Generative AI Essentials: Easy Interview Practice 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: Fundamentals of Generative AI

   Which of the following best describes what Generative AI does?

   • A. It creates new data that resembles examples it has seen during training.
   • B. It only classifies images into categories.
   • C. It deletes unimportant features from data.
   • D. It measures the speed of a computer program.
   • E. It prevents networks from overfitting.
   Show correct answer

   Correct answer: A. It creates new data that resembles examples it has seen during training.

2. Question 2: Common Model Types

   Which type of neural network is commonly used in image synthesis by Generative AI systems?

   • A. Recurrent Neural Network (RNN)
   • B. Generative Adversarial Network (GAN)
   • C. Decision Tree
   • D. Naive Bayes Model
   • E. Random Forest
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   Correct answer: B. Generative Adversarial Network (GAN)

3. Question 3: Text Generation Example

   If an AI model produces a new poem after reading many poems, which task is it performing?

   • A. Summarization
   • B. Clustering
   • C. Generation
   • D. Translation
   • E. Segmentation
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   Correct answer: C. Generation

4. Question 4: Key Transformer Component

   What main mechanism in Transformer models allows them to focus on relevant words when processing a sentence?

   • A. Pooling
   • B. Activation function
   • C. Attention
   • D. Dropout
   • E. Regression
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   Correct answer: C. Attention

5. Question 5: Training Technique

   Transfer learning, often used in Generative AI, involves which of the following?

   • A. Training a brand new model from scratch for each task
   • B. Deleting previous model weights before training
   • C. Using a pre-trained model as a starting point for a new problem
   • D. Generating random data and using it to train the model
   • E. Only using labels with typos for learning
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   Correct answer: C. Using a pre-trained model as a starting point for a new problem

6. Question 6: Real-World Applications

   Which is a common application of Generative AI in the music industry?

   • A. Detecting plagiarism in songs
   • B. Classifying genres based on lyrics
   • C. Composing original melodies based on learned patterns
   • D. Correcting spelling errors in band names
   • E. Storing song lyrics in a database
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   Correct answer: C. Composing original melodies based on learned patterns

7. Question 7: Ethical Considerations

   One ethical concern with Generative AI is 'hallucination.' What does this refer to?

   • A. The model plays music too loudly
   • B. The model generates information that is factually incorrect
   • C. The model falls asleep during training
   • D. The model creates images without colors
   • E. The model only works at night
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   Correct answer: B. The model generates information that is factually incorrect

8. Question 8: Popular Architectures

   Which architecture is most commonly used for large language models, such as ones that can generate long text passages?

   • A. Support Vector Machine (SVM)
   • B. Transformer
   • C. K-Means
   • D. Linear Regression
   • E. Feedforwad Network
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   Correct answer: B. Transformer

9. Question 9: Optimization Techniques

   Hyperparameter tuning in model training refers to which activity?

   • A. Adjusting the input data to be larger than memory allows
   • B. Randomly shuffling the layers in a network
   • C. Selecting the best values for model settings like learning rate and batch size
   • D. Turning off all model parameters before evaluation
   • E. Choosing labels with intentional misspellings
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   Correct answer: C. Selecting the best values for model settings like learning rate and batch size

10. Question 10: Bias in AI Models

    If a language model generates stereotypical responses about a particular group, which issue does this demonstrate?

    • A. Overfitting
    • B. Bias
    • C. Underfitting
    • D. Regularization
    • E. Hyperparmetrization
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    Correct answer: B. Bias