Generative AI interview questions
Generative AI interview Questions and Answers
Basic Level
1. What is Generative AI?
Answer: Generative AI refers to artificial intelligence that can create new content, such as text, images, or audio, based on patterns learned from existing data.
2. What are the typical applications of Generative AI?
Answer: Common applications include text generation, image synthesis, music composition, and content creation for games or virtual environments.
3. What is a GAN?
Answer: GAN stands for Generative Adversarial Network, a model that generates new data by pitting two neural networks against each other: a generator and a discriminator.
4. Explain the structure of a GAN.
Answer: A GAN comprises a generator that creates data and a discriminator that evaluates it. The generator aims to fool the discriminator, while the discriminator seeks to identify real versus fake data correctly.
5. What is a Variational Autoencoder (VAE)?
Answer: A VAE is a generative model that learns to encode input data into a latent space and then decode it to produce new data similar to the original.
6. How do GANs differ from VAEs?
Answer: GANs use adversarial training between a generator and discriminator, while VAEs use probabilistic approaches to learn a smooth latent space for generating data.
7. What is the latent space in generative models?
Answer: Latent space is a compressed, abstract representation of data that captures its key features. It allows for the exploration and generation of new data points.
8. What are the main challenges in training GANs?
Answer: Challenges include mode collapse, training instability, and balancing the learning of the generator and discriminator.
9. What is mode collapse?
Answer: Mode collapse occurs when the generator produces limited data variations, reducing the generated outputs' diversity.
10. How can mode collapse be mitigated?
Answer: Techniques include minibatch discrimination, adding noise, using different loss functions, and employing multiple generators.
11. What is the role of the discriminator in a GAN?
Answer: The discriminator’s role is distinguishing between accurate and generated data, guiding the generator to improve its outputs.
12. What is overfitting in generative models?
Answer: Overfitting occurs when the model learns the training data too well, leading to poor generalization of new, unseen data.
13. What are some ways to prevent overfitting in generative AI models?
Answer: Techniques include regularization, dropout, data augmentation, and using more diverse training data.
14. What is the purpose of using noise in GANs?
Answer: Noise serves as the input to the generator, allowing it to produce diverse and varied outputs.
15. What is a conditional GAN (cGAN)?
Answer: A cGAN is a variant of GAN where the generator and discriminator are conditioned on additional information, such as labels, allowing for controlled generation of specific outputs.
16. How do cGANs differ from standard GANs?
Answer: In cGANs, the generator receives both noise and a conditioning variable, enabling it to produce outputs that align with the provided condition.
17. What is an autoencoder?
Answer: An autoencoder is a neural network that learns to compress data into a lower-dimensional latent space and then reconstruct it back to its original form.
18. What is the difference between an autoencoder and a VAE?
Answer: While both compress and reconstruct data, a VAE uses a probabilistic approach, learning a distribution over the latent space, which allows for more controlled data generation.
19. What are deepfakes?
Answer: Deepfakes are synthetic media, often videos or images, created using AI techniques like GANs to replace one person’s likeness with another’s convincing.
20. What is the ethical concern surrounding deepfakes?
Answer: Deepfakes can be used for malicious purposes, such as spreading misinformation or creating fake news, leading to ethical concerns about privacy and consent.
21. What is data augmentation, and why is it essential in generative AI?
Answer: Data augmentation involves creating variations of training data to increase its size and diversity, improving the model’s generalization ability.
22. How is Generative AI different from traditional AI?
Answer: Traditional AI focuses on classification and prediction tasks, while generative AI creates new content similar to the training data.
23. Why do we need generative AI?
Answer: Generative AI is useful in many areas and for many people. These models can work with different types of input, such as text, images, audio, video, or code, and create new content in any of these formats. For example, it can turn text into an image, an image into music, or a video into written text.
24. What is self-attention?
Answer: Self-attention is a mechanism where a model weighs the importance of different parts of the input data, enabling it to focus on relevant information when generating outputs.
25. What is a language model?
Answer: A language model predicts the probability of a sequence of words, often used in generative AI, to create coherent and contextually appropriate text.
26. How do autoregressive models work?
Answer: Autoregressive models generate sequences by predicting the next item in a sequence based on previous items, often used in text and music generation.
27. What is OpenAI’s GPT?
Answer: GPT (Generative Pretrained Transformer) is a large-scale language model developed by OpenAI that is known for its ability to generate human-like text.
28. What are the main components of the Transformer architecture?
Answer: The Transformer architecture consists of an encoder and decoder, each comprising layers of self-attention and feedforward neural networks.
29. What is a BERT model?
Answer: BERT (Bidirectional Encoder Representations from Transformers) is a model designed to understand the context of a word in both directions. It is used primarily for NLP tasks like question answering and sentiment analysis.
30. What is the difference between GPT and BERT?
Answer: GPT is a unidirectional model used for text generation, while BERT is bidirectional, focusing on understanding the context in both directions for tasks like classification.
31. What is the role of a generator in a GAN?
Answer: The generator’s role is to create data that is as close to natural as possible, aiming to fool the discriminator into thinking it is accurate.
32. What is pixel-wise loss in generative models?
Answer: Pixel-wise loss measures the difference between corresponding pixels in the generated and authentic images, often used in image synthesis tasks.
33. What is the main advantage of using GANs?
Answer: GANs are particularly powerful for generating highly realistic and detailed data, making them useful for image and video generation applications.
34. How does a discriminator learn during GAN training?
Answer: The discriminator learns by distinguishing between accurate and generated data, improving its ability to identify fakes over time.
35. What is the importance of the learning rate in GAN training?
Answer: The learning rate controls the speed at which the model learns. A balanced learning rate is crucial for stable training and avoiding issues like mode collapse.
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Generative AI Interview Questions
Advanced Level
71. What are score-based generative models?
Answer: Score-based generative models use score-matching techniques to estimate the gradients of the data distribution, enabling the generation of new data samples by following the score function.
72. How do autoregressive image models like PixelCNN work?
Answer: Autoregressive image models generate images pixel by pixel, predicting each pixel’s value based on previously generated pixels, allowing for high-quality and detailed image synthesis.
73. What is the role of contrastive learning in generative models?
Answer: Contrastive learning is used to learn representations by comparing positive and negative samples, improving the model’s ability to distinguish between similar and dissimilar data points.
74. How do flow-based generative models differ from GANs and VAEs?
Answer: Flow-based models learn an invertible mapping between the data and latent space, allowing for exact likelihood computation and more controlled generation compared to GANs and VAEs.
75. What is the importance of invertibility in flow-based models?
Answer: Invertibility allows for data generation and reconstruction, enabling exact computation of likelihoods and facilitating more stable and interpretable generative processes.
76. What is an energy-based model (EBM) in generative AI?
Answer: An EBM assigns an energy score to data points, with lower energy indicating higher likelihood. The model generates new data by sampling from low-energy regions of the data distribution.
76.what is the role of MLOPS in Gen ai ?
MLOps (Machine Learning Operations) simplifies the deployment, maintenance, and monitoring of AI models, which is a key component of generative AI.It guarantees scalable, dependable, and consistently updated generative AI models in industrial settings. Teams are able to effectively manage the whole lifecycle of generative AI systems, from data preparation to model deployment and performance monitoring, thanks to MLOps, which makes it easier to integrate and deliver AI models continuously.As a result, generative AI solutions are more reliable, consistent, and effective.
77. How do diffusion models ensure stability in generative processes?
Answer: Diffusion models gradually add and remove noise during generation, enabling more stable training and reducing issues like mode collapse commonly seen in GANs.
78. What is a denoising score-matching model?
Answer: A denoising score-matching model learns to estimate the gradient of the data distribution by minimizing the difference between the noisy data and the true data distribution.
79. What are normalizing flows, and how are they used in generative modelling?
Answer: Normalizing flows is a generative model that transforms a simple distribution into a complex one using a series of invertible mappings, allowing for flexible and controlled data generation.
80. How do implicit generative models differ from explicit generative models?
Answer: Implicit generative models, like GANs, generate data without explicitly defining a probability distribution, while explicit models, like VAEs, define a distribution and sample from it.
81. What is the significance of the reparameterization trick in VAEs?
Answer: The reparameterization trick allows gradients to flow through the stochastic sampling process in VAEs, enabling end-to-end training of the model using backpropagation.
82. What is the purpose of the adversarial loss in GANs?
Answer: The adversarial loss drives the generator to produce realistic data by penalizing it when the discriminator correctly identifies fake data, encouraging the generator to improve.
83. How do transformer-based generative models handle out-of-vocabulary words?
Answer: Transformer-based models use subword tokenization techniques, like Byte-Pair Encoding (BPE), to handle out-of-vocabulary words by breaking them into smaller, known units.
84. What is the role of multi-head attention in Transformers?
Answer: Multi-head attention allows the model to focus on different parts of the input simultaneously, capturing various aspects of the data and improving the generation of complex sequences.
85. How does a Wasserstein GAN (WGAN) differ from a traditional GAN?
Answer: WGANs use a different loss function based on the Wasserstein distance, providing more stable training and mitigating issues like mode collapse in traditional GANs.
86. How are generative AI trained?
Answer: It is a repeated process where the training data is shown to the model, its settings are changed, and it is refined over time to get the right results. The training phase is key to unlocking the full power of generative AI and expanding the limits of artificial creativity.
87. How do VAEs balance reconstruction loss and KL divergence?
Answer: VAEs optimize a loss function that balances the reconstruction loss, which measures how well the model reproduces the input, with KL divergence, which regularizes the latent space to follow a Gaussian distribution.
88. What is the role of entropy in generative models?
Answer: Entropy measures the uncertainty in the model’s predictions, with higher entropy indicating more diverse outputs. Generative models often seek to balance entropy to produce varied but realistic data.
89. How do diffusion models differ from score-based models?
Answer: Diffusion models explicitly model adding and removing noise to data, while score-based models estimate the gradient of the data distribution directly, leading to different approaches in generation.
90. What is a mixture density network in generative modelling?
Answer: A mixture density network outputs parameters for a mixture of Gaussian distributions, allowing the model to capture multimodal data distributions and generate diverse outputs.
91. How does temperature scaling affect generative models?
Answer: Temperature scaling adjusts the randomness in the sampling process, with lower temperatures leading to more conservative outputs and higher temperatures producing more diverse and creative results.
92. What is a variational lower bound in VAEs?
Answer: The variational lower bound is an objective function approximating the data distribution, balancing reconstruction quality and regularization in VAEs.
93. What is the role of KL divergence in VAEs?
Answer: KL divergence measures the difference between the learned latent distribution and a prior distribution, regularizing the latent space to ensure meaningful and smooth generation.
94. What are the benefits of using a hybrid generative model?
Answer: Hybrid models combine generative approaches, like VAEs and GANs, to leverage their strengths and produce higher-quality, more stable outputs.
95. How do autoregressive models like GPT generate text?
Answer: Autoregressive models generate text by predicting the next token in a sequence based on previous tokens, building the output word by word or token by token.
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96. What is the significance of the latent space in generative models?
Answer: The latent space represents compressed, abstract features of the data, allowing the model to capture complex structures and generate new samples by navigating this space.
97. How do hierarchical VAEs differ from standard VAEs?
Answer: Hierarchical VAEs introduce multiple levels of latent variables, allowing for more complex and structured data representations and improving generation quality.
97. What is the importance of mode collapse in GANs in prompt engineering?
Answer: Mode collapse in GANs is crucial in prompt engineering because it leads to a lack of diversity in generated outputs, where the model produces similar results for different prompts. Addressing mode collapse is essential to ensure that the AI generates varied and creative responses, improving the effectiveness and richness of the prompts used.
98. What is the role of the discriminator in a WGAN-GP?
Answer: The discriminator in a WGAN-GP ensures the generator produces realistic data by evaluating the quality of generated samples and guiding the generator through a gradient penalty term that stabilizes training.
99. How do contrastive divergence methods apply to generative models?
Answer: Contrastive divergence trains models like RBMs by approximating the log-likelihood gradient, helping the model learn to generate data that closely matches the training distribution.
100. What is the importance of mode collapse in GANs?
Answer: Mode collapse occurs when the generator produces limited diversity in outputs, focusing on a few modes of data distribution. Addressing mode collapse is crucial for generating varied and realistic data.
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