"Go has simpler syntax than Java. It is light weight. It is not Object Oriented. It does not support function overloading and function overriding. But these are small technical differences. Both are similar when it comes to testing. You have to create a mock object and implement an interface. Functionally, I did not feel any major difference."
Vishal T. - "Go has simpler syntax than Java. It is light weight. It is not Object Oriented. It does not support function overloading and function overriding. But these are small technical differences. Both are similar when it comes to testing. You have to create a mock object and implement an interface. Functionally, I did not feel any major difference."See full answer
"SDLC stands for Software Development Life Cycle, which is a process used by software development teams to design, develop, test, and deploy high-quality software products. There are several SDLC models, including the Waterfall model, Agile model, and DevOps model. Here's an overview of each model and how I would implement it:
Waterfall Model: This model is a linear sequential approach, where each stage of the software development process must be completed before moving on to the next stage. T"
Anonymous Panda - "SDLC stands for Software Development Life Cycle, which is a process used by software development teams to design, develop, test, and deploy high-quality software products. There are several SDLC models, including the Waterfall model, Agile model, and DevOps model. Here's an overview of each model and how I would implement it:
Waterfall Model: This model is a linear sequential approach, where each stage of the software development process must be completed before moving on to the next stage. T"See full answer
"Understand the problem:Users in areas with poor or no data coverage can't fully utilize their smartphones.
This affects communication, access to information, and use of various apps.
Potential solutions:
a) Offline capabilities:
Enhance offline modes for popular apps (maps, messaging, content consumption).
Implement better caching mechanisms to store frequently accessed data.
Develop a system for queuing actions to be executed once coverage is restored.
b) Improved connectivity:
-"
Surbhi G. - "Understand the problem:Users in areas with poor or no data coverage can't fully utilize their smartphones.
This affects communication, access to information, and use of various apps.
Potential solutions:
a) Offline capabilities:
Enhance offline modes for popular apps (maps, messaging, content consumption).
Implement better caching mechanisms to store frequently accessed data.
Develop a system for queuing actions to be executed once coverage is restored.
b) Improved connectivity:
-"See full answer
"Drew the following framework - Maturity of the tech (if it is too mature then consider buying), Competitors (how many, when are they launching this new tech etc.), Market Share of competitors, Financial considerations (build vs buy NVP), Collaborators for this tech, Economic Climate (Anti-trust, crisis etc.)"
Joohi M. - "Drew the following framework - Maturity of the tech (if it is too mature then consider buying), Competitors (how many, when are they launching this new tech etc.), Market Share of competitors, Financial considerations (build vs buy NVP), Collaborators for this tech, Economic Climate (Anti-trust, crisis etc.)"See full answer
"When we collect too much user data or survey responses, we risk creating data overload, which can hinder data analysis by clouding important insights with excessive, unnecessary information. This could make it more difficult to discern meaningful patterns or trends from the data, adding complexity to our data processing tasks and potentially leading to incorrect conclusions or strategies.
Furthermore, over-collecting data may compromise users' privacy and trust. If users find out that a company"
Surbhi G. - "When we collect too much user data or survey responses, we risk creating data overload, which can hinder data analysis by clouding important insights with excessive, unnecessary information. This could make it more difficult to discern meaningful patterns or trends from the data, adding complexity to our data processing tasks and potentially leading to incorrect conclusions or strategies.
Furthermore, over-collecting data may compromise users' privacy and trust. If users find out that a company"See full answer
"Metrics which Youtube Consider before building a recommender system
Number of likes on a video by user
The watch time of a video by the user
The video disklied by the user
The video share by a user
The video skipped or churn with 20-30 seconds.
Depending on this Youtube build a recommender system. The video suggestion feature in youtube works based on the recommender system. It may use a hybrid of batch prediction and online prediction. So depending on the above metrics, the youtube p"
Anonymous Muskox - "Metrics which Youtube Consider before building a recommender system
Number of likes on a video by user
The watch time of a video by the user
The video disklied by the user
The video share by a user
The video skipped or churn with 20-30 seconds.
Depending on this Youtube build a recommender system. The video suggestion feature in youtube works based on the recommender system. It may use a hybrid of batch prediction and online prediction. So depending on the above metrics, the youtube p"See full answer
"This is a Technical question. It tests your ability to understand high level technical concepts. Even though your job won't have any coding involved, you'll still need to understand these concepts. Being able to cover all these topics with clarity communicates confidence in your interviewer.
Unfortunately, there's no formula for technical questions, but some general tips are:
Use analogies when you can
Break your solution into clear, bite-size steps
Don't be afraid to use examples to b"
Exponent - "This is a Technical question. It tests your ability to understand high level technical concepts. Even though your job won't have any coding involved, you'll still need to understand these concepts. Being able to cover all these topics with clarity communicates confidence in your interviewer.
Unfortunately, there's no formula for technical questions, but some general tips are:
Use analogies when you can
Break your solution into clear, bite-size steps
Don't be afraid to use examples to b"See full answer
"This is due to sticky sessions.
The load balancer is not correctly configured with sticky session option.
It is likely the servers were storing session data on the server themselves (in-memory), and thus when user makes a request, the load balancer routes this to a different server than the one they started with, that second server may not recognise the user's session. This could prompt the user to log in again.
One way to resolve this, is to use a centralised session storage, something like"
T I. - "This is due to sticky sessions.
The load balancer is not correctly configured with sticky session option.
It is likely the servers were storing session data on the server themselves (in-memory), and thus when user makes a request, the load balancer routes this to a different server than the one they started with, that second server may not recognise the user's session. This could prompt the user to log in again.
One way to resolve this, is to use a centralised session storage, something like"See full answer
"So Machine learning provides the ability to machines to learn patterns from large data. So applying the same when needed. So the type of algorithm depends on the requirement/use case or the data.
For example, if we have data that is labeled and we need to do classification then we will go and perform logistic regression but if we want prediction instead of classification, then we will go and build a regression model.
In case the data is not labeled, then we can go ahead and build a model usin"
Anonymous Muskox - "So Machine learning provides the ability to machines to learn patterns from large data. So applying the same when needed. So the type of algorithm depends on the requirement/use case or the data.
For example, if we have data that is labeled and we need to do classification then we will go and perform logistic regression but if we want prediction instead of classification, then we will go and build a regression model.
In case the data is not labeled, then we can go ahead and build a model usin"See full answer