Interview Questions

Cj K. - "Clarifying Q Is the search traffic meaning # visits clicked through the 'search' button from the homepage(search CTR)? or does this include # pages viewed after the search result pages (search depth)? Hypothesis I believe introducing Gen AI as a default feature on the Google's search box will increase #search traffic. I assume the biggest user pain point within current google homepage is that deciding on a keyword for search. Unlike other alternative platforms such as"See full answer

Anonymous Ferret - "i do feel like the question itself is kind of confusing. Youtube does have a product called YouTube’s Analytics, which is a channel analytics tool for creator lol"See full answer

Nishtha - "How much does the Empire State Building weigh? I will estimate the weight of the Empire State Building using a structured approach. I’ll start with clarifying questions, define an equation, make reasonable assumptions, perform calculations, and address trade-offs and potential errors. Clarifying Questions To refine the scope, I considered: How many floors does the Empire State Building have? What is the area per floor? What materials are used (e.g., steel, concrete)"See full answer

Megha V. - "Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss. It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"See full answer

Lucas G. - "Because testing many engagement metrics at once increases the risk of finding effects that aren't real (the 'multiple comparisons problem'), you must adjust your criteria for statistical significance. For social media data, the Benjamini-Hochberg procedure is often a practical choice as it controls the rate of false discoveries (FDR) while still allowing you to detect genuine changes; however, the ideal adjustment method will vary depending on your specific number of metrics (e.g., use Bonferron"See full answer

Lucas G. - "To speed up A/B tests results with limited sample sizes, we can apply advanced techniques like CUPED to reduce variance for faster statistical significance, interleaving to gather more comparative data per user (e.g., ranking), MAB to dynamically allocate traffic to winning variations for quicker optimization (e.g., campaigns), and Bayesian A/B testing which offers probabilistic conclusions that can be reached earlier. Each method, when appropriately applied, allows you to gain m"See full answer

Steve Y. - "Define: How is daily post view calculated Isolate Issues: Data issue Time period Geo IOS vs Android vs Web Correlated Metrics in the funnel DAU Time spent/ scrolls Engagement - likes, comments External factors Competitor actions Big events Internal factors Product launch Feature change"See full answer

Rafał P. - "def changeString(org: str,target:str) -> bool: lOrg = len(org) lTarget = len(target) \# They have to be equal in lenght if lOrg != lTarget: return False counter1 = Counter(org) counter2 = Counter(target) \# Counter internally iterates through the input sequence, counts the number of times a given object occurs, and stores objects as keys and the counts as values. if counter1 != counter2: return False diff = sum(org[i] != target[i] for i in range(n)) return diff == 2 or (diff == 0 and any(v > 1 f"See full answer