Concept Interview Questions

Amney M. - "it is really good explanation thanks it is really good explanation thanks"See full answer

Amy M. - "Clarifying questions: Do we want to focus on front end or backend? Front end Do we want to focus on any particular platform? For ex: Site, mobile, apps Interviewer: Desktop Is there anything tools on gmail that you'd like me to focus on? For ex: Meet, Hangouts, Notes Interviewer: Just the main product Are there any specific product buckets that you'd like me to go through? For ex: Sign up flows, login flows, security, product experience, sign out flow, recommend"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

Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer

Sue G. - "Abstract class A class that can have Abstract methods - without implementations and Concerete Methods i.e with implementation. Can have private, protected and public access modifiers. Supports Single inheritance i.e a class can extend only 1 abstract class Can have constructors Mainly used when sharing common behaviors Interface Class A collection of abstract methods ( can have static and default methods also - onwards of java 8) Public, static, final are the access"See full answer

Maedu E. - "Imagine a blockchain as a magical, unchangeable diary that keeps track of all the candies you share with your friends. Whenever you share a candy, you write it down in this special diary, and your friends also write it down in their diaries. But here's the cool part – all the diaries are connected and can talk to each other! So, when you want to know who has borrowed your candy or if you borrowed candy from someone else, you just check this special diary. It shows you the history of all the can"See full answer

Anonymous Aardvark - "in simple words, linear regression helps in predicting the value whereas logistics regression helps in predicting the binary classification. But lets talk through some example Linear regression model: E-commerce website pricing recommendation engine is built on linear regression model where we do have some variables such as competitor price, internal economics and consumer demand etc when we put this in a supervised learning model, it helps in predicting prices Logistics regression model"See full answer

Hari S. - "Clarifying questions: Is this a standalone product or a Meta integrated product? Which region is this for? US or another region or global? Is this for a county run library or a more informal group community library? Assumptions: It's a meta integrated product US based system group run community library Let's see if a lending library fits into Meta's company mission to build community and bring people closer together. Yes, a lending library does build community by helping peop"See full answer

Will I. - "Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer? The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."See full answer

Erjan G. - "1) select avg(session) from table where session> 180 2) select round(sessiontime/300)*300 as sessionbin, count() as sessioncount from table group by round(sessiontime/300)300 order by session_bin 3) SELECT t1.country AS country_a, t2.country AS country_b FROM ( SELECT country, COUNT(*) AS session_count FROM yourtablename GROUP BY country ) AS t1 JOIN ( SELECT country, COUNT(*) AS session_count FROM yourtablename `GROUP BY countr"See full answer

Michael F. - "As an interviewer, I have asked this question to candidates in the past. Here are the major topics I am looking for in an interview The candidate should understand that there are ways of measuring the loss of a particular clustering. For example, we can take the average distance of each point to it's cluster center. The candidate should understand that this loss will always decrease as the number of clusters increases. For that reason, we can't just pick the value of K that minimizes the l"See full answer

Rohan S. - "I got to play with a VR for the first time a few weeks ago at my friend’s house and I loved it, so this is a really fun question. Clarifying Questions Is this something we are looking to create Microsoft or are we a startup company etc? > Education can refer to elementary, high school, university, or even self-learning (e.g. Udemy). Is there a specific area we want to focus on? << What do y"See full answer

Ankita G. - "Merge Sort"See full answer

Pree M. - "Let's split the experience in four parts: Time between order is placed and order is waiting to be prepped by the shopper. Time between order is prepped by the shopper and ready to be picked by the driver. Time when it's out for delivery. Post delivery experience. Now lets think through what can do wrong? How do we define wrong? A wrong would be anything where a customer has a negative experience. Let's dive into the when things could potentially go wrong. **order is placed and"See full answer

Meenakshi D. - "BETWEEN and HAVING clauses in SQL serve different purposes: 1. BETWEEN Clause Used to filter rows based on a range of values. Works with numeric, date, or text values. Can be used with WHERE or HAVING clauses. The range includes both lower and upper bounds. Example: Filtering employees with salaries between 30,000 and 50,000 `SELECT * FROM Employees WHERE salary BETWEEN 30000 AND 50000;` 2. HAVING Clause Used to filter **groups"See full answer

Garima B. - "I faltered on this question. I haven't given a PMM interview in a very long time since I moved from marketing to pure product management. I chose DownDog Yoga App as my preferred app, since I use either either their sleep meditations or the yoga app thrice a week. The feature that was introduced was Aerial Yoga (the interviewer made it as a feature) I went with the assumption that Aerial Yoga is something that needs to be driven off-line rather than online - requires studio space, infrastructr"See full answer

Taha U. - "In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."See full answer

Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer

Constantin P. - "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"See full answer