Hi Everyone welcome to the blog where you are gonna see Here are top 12 TCS Data Science Gen AI Interview Question for experienced lateral entry candidates. Most asked TCS Data Science Gen AI Interview Question with answers. I have personally appeared on this interview, and I am sharing my experience with this. I have attended many other companies interviews also. I will share those in subsequent posts too.
Here is the Link to get different Interview Experience for Data Science and Gen AI. Please take a note that, prepare questions according to your resume. You must know all the things you have written in resume. Apart from this, you must be prepared for any generic questions related to ML, AI and Gen AI. Lets start.
So at the beginning, as usual interviewer started with his introduction and greetings. Then he asked my introduction. So, here you need to highlight points and projects written in your resume. We shall always try to put emphasis on that project which we are more confident to explain. Once introduction is over, interviewer straight forward started asking questions from gen AI and ML projects which I had done. Here are some questions.
Table of Contents
Why do we need Gen AI and why can’t we handle the use case using Deep Learning? What difference you see in LLM and Deep Learning?
Generative AI refers to systems that can generate new content, such as text, images, music, or code, from existing data. It leverages models like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or LLMs (Large Language Models) to produce outputs that mimic the training data. Traditional AI, on the other hand, focuses on classifying or predicting outcomes based on input data rather than generating new content. Generative AI is more creative and can synthesize new data, while traditional AI is typically more analytical. So. the use case was related to generating some content, so we used Large Language Models instead of predictive models or classifying models in DL and ML.
What are the key components of a Generative Adversarial Network (GAN)?
GANs consist of two neural networks:
- Generator: Creates new data samples similar to the training data.
- Discriminator: Evaluates whether the generated data is real or fake (from the training data). The two networks compete: the generator tries to fool the discriminator, and the discriminator improves at detecting fake data. Over time, the generator learns to produce increasingly realistic data.
How does Retrieval-Augmented Generation (RAG) improve language model performance?
RAG enhances language models by combining the strengths of retrieval-based systems and generative models. Instead of relying solely on a generative model to predict an answer, RAG retrieves relevant documents or information from a database (like a knowledge base or vector store) and uses this retrieved data as context to generate more accurate and informed responses. This improves factual accuracy and relevance, especially for knowledge-intensive tasks.
What is Zero-Shot Learning and how is it useful in Generative AI?
Zero-shot learning refers to a model’s ability to perform a task without having seen any labeled examples of that task during training. This is particularly useful in Generative AI because it allows models to generate relevant responses or solutions to tasks that were not part of the training data. Zero-shot learning is enabled by the generalization ability of models like GPT-3, which can leverage pre-trained knowledge and apply it to new tasks based on contextual cues and instructions.
What is the role of a vector database in a Generative AI pipeline?
A vector database stores data in vectorized format, where each item (e.g., text, image, or audio) is represented as a high-dimensional vector. In a Generative AI pipeline, a vector database is used to:
- Store Embeddings: Store vector embeddings of data for fast retrieval and similarity searches.
- Support RAG: When combined with RAG models, a vector database can quickly retrieve relevant documents or context based on user queries. This enhances the model’s ability to generate more accurate and context-aware responses. Popular vector databases include FAISS, Pinecone, and ChromaDB.
What are attention mechanisms in neural networks, and how are they used in Transformer models?
Attention mechanisms allow models to focus on specific parts of the input sequence when generating output. In Transformer models, the attention mechanism calculates the importance of each word relative to others in a sentence, enabling the model to capture long-range dependencies and context. This is crucial for tasks like machine translation, where understanding the relationships between distant words improves the quality of the output. Self-attention is a core component of Transformer models like GPT and BERT, helping them handle large sequences of text more efficiently.
How do you handle outliers in a dataset before building a predictive model?
Outliers can distort model training and affect performance. To handle outliers:
- Visualization: Use boxplots or scatterplots to detect outliers.
- Statistical Techniques: Apply Z-scores or the IQR (Interquartile Range) method to identify and treat outliers.
- Transformation: Log transformations or normalization can reduce the effect of extreme values.
- Removal or Imputation: Depending on the context, outliers can be removed or imputed with median or other central values. The choice depends on whether the outliers are genuine anomalies or data errors.
Why do we need activation functions in neural networks, and how would a neural network behave without them?
Activation functions introduce non-linearity into the neural network, which is crucial for the network to model complex patterns in data. Without activation functions, the network would simply become a linear transformation of the input data, no matter how many layers are added. This would prevent the network from learning non-linear relationships and limit its ability to solve problems like image recognition, language modeling, or other complex tasks. Common activation functions include ReLU, Sigmoid, and Tanh.
Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff describes the relationship between a model’s ability to generalize to unseen data:
- Bias refers to errors due to overly simplistic models. High bias models (like linear regression) may underfit the data, missing important relationships and producing high training and test errors.
- Variance refers to errors due to overly complex models that adapt too closely to the training data. High variance models (like deep neural networks) may overfit, performing well on training data but poorly on unseen test data. Achieving a balance between bias and variance is crucial for a model to generalize well.
What is the purpose of regularization in machine learning models, and what are some common techniques?
Regularization is used to prevent overfitting in machine learning models by penalizing large model weights. This encourages simpler models that generalize better to unseen data. Common regularization techniques include:
- L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the weights, encouraging sparsity in the model.
- L2 Regularization (Ridge): Adds a penalty equal to the squared value of the weights, discouraging large weights without forcing them to zero.
- Dropout: Randomly drops units (neurons) during training, forcing the network to be more robust and reducing overfitting.
- Early Stopping: Monitors the performance on a validation set during training and stops when performance starts to degrade.
What is the vanishing gradient problem, and how does it affect deep learning models?
The vanishing gradient problem occurs when the gradients of the loss function with respect to the weights become very small during backpropagation, especially in deep networks with many layers. This can cause the weights in the earlier layers to update very slowly or not at all, leading to poor learning. This problem is particularly severe in networks using sigmoid or tanh activation functions. Solutions include:
- Using the ReLU (Rectified Linear Unit) activation function, which helps alleviate the problem because its derivative is either 1 or 0 for positive inputs.
- Batch normalization and residual connections (as in ResNet) can also help by normalizing the inputs and allowing gradients to flow more easily.
After these all questions, the interviewer told he is fine with the theoretical areas of Gen AI and AI. He wants to check Python knowledge. So he asked me to share my screen over teams and open any notepad. Then he gave following coding question. Before starting the coding round he asked me how much I rate myself in python coding out of 10. I rated myself as 7 because I did not wanted to be in situation where I rated myself higher and not able to solve the coding challenge.
Find the Second Largest Number in a List
def second_largest(numbers):
# Remove duplicates by converting to a set and then back to a list
unique_numbers = list(set(numbers))
# Sort the list in descending order
unique_numbers.sort(reverse=True)
# Return the second largest number
return unique_numbers[1]
# Example usage
numbers = [5, 1, 5, 3, 4, 2]
print(second_largest(numbers)) # Output: 4
Guys, so this was the only python questions asked by him and then he said, HR will get back to me. If I have any questions I can ask. I asked question related to role for current interview and he explained that well. Later I got the call for one more MR round of interview and then HR asked me for documentation. They had released the Offer also.
Thank you for going through the questions and interview experience. Hopefully it was helpful for you guys.