"This is a Favorite Product question with a slight twist. We have a constraint: picking an enterprise app. Let's go over the Favorite Product formula before we begin:
Choose a product and briefly explain what it is
Who are the users?
What are their pain points?
How did competitors solve it in the past?
How does this product address these pain points differently and better?
How would you improve it/make it better?
Summarize
Let's start!
Choo"
Exponent - "This is a Favorite Product question with a slight twist. We have a constraint: picking an enterprise app. Let's go over the Favorite Product formula before we begin:
Choose a product and briefly explain what it is
Who are the users?
What are their pain points?
How did competitors solve it in the past?
How does this product address these pain points differently and better?
How would you improve it/make it better?
Summarize
Let's start!
Choo"See full answer
"This is a Measure Success question with a slight twist. The twist here is we need to consider a hypothetical change rather than one already built. This changes our formula slightly - specifically we may not be able to apply a UX flow to drive analysis since we're unsure of the implementation. Instead, we'll look at core behaviors that are indicative of success. Here's the modified formula:
Ask clarifying questions
State the goal of the feature
**Apply a UX flow to drive an"
Exponent - "This is a Measure Success question with a slight twist. The twist here is we need to consider a hypothetical change rather than one already built. This changes our formula slightly - specifically we may not be able to apply a UX flow to drive analysis since we're unsure of the implementation. Instead, we'll look at core behaviors that are indicative of success. Here's the modified formula:
Ask clarifying questions
State the goal of the feature
**Apply a UX flow to drive an"See full answer
"What do all data scientists need to know about how to work with very large datasets?
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Corrin Lakeland
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, M.S. Data Science, University of St. Thomas, St. Paul (2018)6yData Science consultant and managerUpvoted by[Tom Halloin](https://www.quora"
Hayatu H. - "What do all data scientists need to know about how to work with very large datasets?
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Corrin Lakeland
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, M.S. Data Science, University of St. Thomas, St. Paul (2018)6yData Science consultant and managerUpvoted by[Tom Halloin](https://www.quora"See full answer
"My ideal PM role will definitely be in that I can work smoothly with the inner team(tech, marketing, sales, etc) whether in collecting requirements or translating these requirements to the responsible tech team.
To have the full hands of a product so that I can have ownership to enhance and improve the product toward the right vision, that matches the company and market vision as well
"
Raneem A. - "My ideal PM role will definitely be in that I can work smoothly with the inner team(tech, marketing, sales, etc) whether in collecting requirements or translating these requirements to the responsible tech team.
To have the full hands of a product so that I can have ownership to enhance and improve the product toward the right vision, that matches the company and market vision as well
"See full answer
"For RAG systems, You need to evaluate the retrieval and generation. Typically, you have golden truth question and answers as evaluation data set.
For retrieval, check if the retrieved contexts are relevant for the question
For generation evaluation, You check the semantic similarity between the golden truth and RAG generated answer.
Apart from this, you can evaluate the output using frameworks like RAGAS where the answers generated are evaluated based on completeness, faithfulness, to"
S R. - "For RAG systems, You need to evaluate the retrieval and generation. Typically, you have golden truth question and answers as evaluation data set.
For retrieval, check if the retrieved contexts are relevant for the question
For generation evaluation, You check the semantic similarity between the golden truth and RAG generated answer.
Apart from this, you can evaluate the output using frameworks like RAGAS where the answers generated are evaluated based on completeness, faithfulness, to"See full answer
"Clarifications: Is it passenger/cargo/defense
Answer: Passenger
Is it direct or stops included - Direct
Last I read London had 80 M passengers annually
Per month - 6.7 M
Per Day - 220 K
Assumption here - London is a hub and it has flights to all continents. Assumptions are the split below
Flights to Europe -- 30%
Flights to USA -- 20%
Flights to Asia -- 30%
Flights to Africa -- 10%
Flights to Australia -- 10%
Dubai is part of Asia - 30% of 220 K is 66K
Within Asia - Passeng"
Ramesh Y. - "Clarifications: Is it passenger/cargo/defense
Answer: Passenger
Is it direct or stops included - Direct
Last I read London had 80 M passengers annually
Per month - 6.7 M
Per Day - 220 K
Assumption here - London is a hub and it has flights to all continents. Assumptions are the split below
Flights to Europe -- 30%
Flights to USA -- 20%
Flights to Asia -- 30%
Flights to Africa -- 10%
Flights to Australia -- 10%
Dubai is part of Asia - 30% of 220 K is 66K
Within Asia - Passeng"See full answer
"I live in a city in California which has about 100,000 people with about 25 -30 gas stations.
California has about 39M people in it.
with these assumptions we can calculate to find x for number of gas stations in the US.
x / 39M x 30/100,000 = 11,700 gas stations"
Pm wizard O. - "I live in a city in California which has about 100,000 people with about 25 -30 gas stations.
California has about 39M people in it.
with these assumptions we can calculate to find x for number of gas stations in the US.
x / 39M x 30/100,000 = 11,700 gas stations"See full answer
"I'm going to assume the problem is limited until the point of where the rider get into the car.
Rider segments/characteristics:
Local rider (lives and travels in the same city)
Tourist rider (visiting a city, using Lyft for the first time in the city)
Lets think about some problems for these types of users:
Local rider: (Choose to prioritize on these use-cases assuming its a larger % of rides overall)
Ride shows as confirmed, but driver does not move on the map
Driver does"
Arya R. - "I'm going to assume the problem is limited until the point of where the rider get into the car.
Rider segments/characteristics:
Local rider (lives and travels in the same city)
Tourist rider (visiting a city, using Lyft for the first time in the city)
Lets think about some problems for these types of users:
Local rider: (Choose to prioritize on these use-cases assuming its a larger % of rides overall)
Ride shows as confirmed, but driver does not move on the map
Driver does"See full answer
"Clarifying Points/Questions\:
What are the specific needs and pain points of hotels and their guests?
How can Google Assistant technology be leveraged to enhance the hotel experience?
What are the potential monetization and revenue opportunities for such a product?
Assumptions\:
Hotels are looking for ways to improve guest experience and operational efficiency.
Guests expect personalized, convenient, and technologically advanced services.
Google Assistant has the capabi"
Anushka D. - "Clarifying Points/Questions\:
What are the specific needs and pain points of hotels and their guests?
How can Google Assistant technology be leveraged to enhance the hotel experience?
What are the potential monetization and revenue opportunities for such a product?
Assumptions\:
Hotels are looking for ways to improve guest experience and operational efficiency.
Guests expect personalized, convenient, and technologically advanced services.
Google Assistant has the capabi"See full answer
"Started with the clarifying questions. Then discussed the following with the panel:
Goal
Users
Use Cases
Features
Priority
Metrics to measure the success"
Apurv M. - "Started with the clarifying questions. Then discussed the following with the panel:
Goal
Users
Use Cases
Features
Priority
Metrics to measure the success"See full answer