Interview Questions

Exponent - "This is another Fermi problem — an estimation or approximation problem with limited information and back-of-the-envelope calculations. There's no right answer: interviewers want to understand how you think and how well you can explain your reasoning, rather than what you already know. Recall the formula for Fermi problems: Ask clarifying questions Catalog what you know Make equation(s) Think about edge cases to add to equation **Breakdown components of your"See full answer

Xuelong A. - "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them. I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."See full answer

Exponent - "Before we begin, it's important to understand what the interviewer is testing with this type of question. This is a Strategy Question that tests an interviewee's ability to create compelling arguments from high level information, discern motive, and think strategically. We can split this question into two parts: Why did Yelp sell Eat24 and why did the stock jump? Why did Yelp sell Eat24? > To start, let's go over some potential reasons why Yelp might sell Eat24:Back to core competencies >"See full answer

Yitayal B. - "import re def naturalsortkey(s): Split the string into a list of text and numbers "item10" -> ["item", 10] return [int(text) if text.isdigit() else text.lower() for text in re.split(r'(\d+)', s)] def natural_sort(arr): return sorted(arr, key=naturalsortkey) \# Example Usage data = ["item10", "item2", "item1", "item20", "2nd item", "1st item"] sorteddata = naturalsort(data) print(f"Standard Sort: {sorted(data)}") print(f"Natural Sort: {sorted_data}") "See full answer

Ayush B. - "focus was on tradeoffs of diff object detection algorithms, data collection and labelling, foundational models, followup: not working well in production, retraining/active learning"See full answer

Chetak C. - "Probability that one of the coupons is used = 1 - Probability that no coupon is used = 1 - nC0 p^0 * (1-p)^n = 1 -(1-p)^n"See full answer

Kevin T. - "When a stakeholder’s request is ambiguous, I start by clarifying the goal and defining what “success” looks like. I ask targeted questions to understand the business problem, the timeframe, the scope/location, and who the analysis is for. Then I confirm definitions (metrics, segments, filters), agree on the expected output format, and restate the request back to them in one sentence before I begin."See full answer