AI-powered Artificial Intelligence Engineer mock interviews

How would you approach handling imbalanced datasets in a classification problem? Can you discuss different techniques you have used in the past to address this issue?

Can you explain the trade-offs between using a linear regression model versus a decision tree model for a given dataset? How would you decide which model to use in a real-world scenario?

How would you approach the task of sentiment analysis on social media data, considering challenges such as sarcasm, slang, and context-dependent sentiments?

Can you explain the concept of word embeddings and discuss the advantages and disadvantages of using pre-trained word embeddings in NLP tasks?

How would you approach analyzing a dataset with missing values and outliers? Share a specific technique or method you have used in the past to handle such data challenges effectively.

Can you explain the concept of p-value and its significance in hypothesis testing? Provide an example where understanding p-value influenced the decision-making process in a real-world scenario.

Explain the concept of dynamic computational graphs in TensorFlow and how it differs from static computational graphs in terms of flexibility and performance.

Can you discuss the advantages and disadvantages of using PyTorch's eager execution mode compared to TensorFlow's default static graph execution?

Describe a situation where you had to communicate a complex idea to a non-technical stakeholder. How did you ensure effective communication and understanding? What strategies did you use to overcome any potential misunderstandings?

Can you provide an example of a time when you had to navigate a challenging team dynamic to successfully deliver a project? How did you approach the situation and what was the outcome?