What Is Asked In Interviews For Data Science With Genertaive AI Roles?

Summary

In this video, Krishak discusses essential preparation strategies for data science and generative AI interviews, drawing on insights from a successful candidate's experience. He highlights the types of questions typically asked, covering topics such as Python, statistics, machine learning, deep learning, and large language models (LLMs). The focus is on practical applications and real-world scenarios, with specific emphasis on inferential statistics and NLP techniques like text embeddings. Krishak also explores frameworks, databases, and project implementation, encouraging viewers to leverage his channel's resources for effective interview preparation.

Takeaways

• 😀 The speaker, Krishak, shares insights from a successful student who recently got hired as a generative AI engineer.
• 📊 Interview preparation should cover a range of topics including Python, statistics, machine learning, deep learning, and natural language processing (NLP).
• 🧪 Basic to intermediate Python skills are essential, with practical tasks assigned during interviews to assess proficiency.
• 📈 Candidates should be well-versed in inferential statistics, particularly hypothesis testing techniques like Z-tests, T-tests, and ANOVA.
• 🔍 A significant portion of the interview focused on large language models (LLMs) and their applications in generative AI roles.
• 💡 Key NLP techniques such as text embeddings, including Word2Vec, should be understood, including how they are trained from scratch.
• 🧠 Familiarity with deep learning concepts like activation functions, loss functions, and optimizers is crucial for interviews.
• 🚀 The Transformer architecture, especially models like BERT, is heavily emphasized, accounting for about 30% of the interview content.
• 🛠️ Understanding open-source versus paid LLMs and the relevant frameworks (e.g., LangChain, LlamaIndex) is important for candidates.
• 📅 Real-world project experience in generative AI is highly valued, so candidates should prepare to discuss their own projects and deployment strategies.

Q & A

What is the main focus of the video?

The video focuses on preparing for generative AI interviews, specifically discussing the types of questions that candidates can expect.

Who is the speaker in the video?

The speaker's name is Krishak, who runs a YouTube channel dedicated to data science and generative AI.

What kind of experience did the student have who recently cleared the interview?

The student had approximately six months of experience, which included only an internship.

What programming language was primarily tested during the interview?

Python was the primary programming language tested, with questions ranging from basic to intermediate levels.

What types of statistics were covered in the interview?

The interview focused on inferential statistics, specifically on hypothesis testing, including tests like Chi-square and ANOVA.

Which natural language processing (NLP) concepts were emphasized?

Key NLP concepts included text embeddings, particularly Word2Vec, and the mathematical concepts such as cosine similarity.

What was the focus regarding deep learning in the interview?

Deep learning topics focused on activation functions, loss functions, optimizers, and specific architectures like Transformers and BERT.

What types of models were discussed related to generative AI?

The discussion included open-source models like Llama 2 and paid models, along with frameworks such as LangChain and Llama Index.

What databases were relevant in the context of the interview?

The interview covered MySQL, NoSQL, and vector databases, highlighting their uses in applications related to generative AI.

How was the structure of the interview divided among topics?

The interview structure included about 20% on basic concepts, 30% on deep learning (mainly Transformers), and 30% on project-related discussions, leaving 20% for other miscellaneous topics.