Concept Interview Questions

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

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

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

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

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

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

Ankita G. - "Merge Sort"See full answer

Nouna J. - "If this question was for a product manager; I would approach it as I don't really know how actually these vehicles work. I will try to simplify the concept and answer questions like why autonomous vehicles are exists and what is the purpose of them. Autonomous vehicle is a vehicle can do some actions on behalf on a human and as main reason is to increase the safety, so to come up into how they work, I should identify the main actions that human does and prioritize it based on what they may"See full answer

Ali H. - "SQL databases are relational, NoSQL databases are non-relational. SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data. SQL databases are vertically scalable, while NoSQL databases are horizontally scalable."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

Surbhi G. - "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt. A co"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

Ilnur I. - "I can try to summarize their discussion as I remembered. Linear regression is one of the method to predict target (Y) using features (X). Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average. This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"See full answer