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posted on 28 Jun 2024
Kubernetes on GKE is a managed Kubernetes service provided by Google Cloud Platform.
GKE stands for Google Kubernetes Engine.
It allows users to deploy, manage, and scale containerized applications using Kubernetes.
GKE provides features such as automatic scaling, monitoring, and logging.
Users can easily create Kubernetes clusters on GKE using the Google Cloud Console or command-line tools.
GKE integrates with other Google...
Yes, Application Servers are software frameworks that provide an environment for running web applications.
Application Servers manage the execution of web applications
They provide services such as security, scalability, and resource management
Examples include Apache Tomcat, JBoss, and Microsoft IIS
Google Cloud is a suite of cloud computing services provided by Google.
Offers a wide range of services including computing, storage, networking, machine learning, and more
Provides tools for data analytics, machine learning, and artificial intelligence
Allows users to build, test, and deploy applications on Google's infrastructure
Offers scalable and flexible pricing options based on usage
Examples: Google Compute Engine,
BigQuery architecture is a serverless, highly scalable, and cost-effective data warehouse designed for big data analytics.
BigQuery separates storage and compute, allowing for independent scaling of each
It uses a distributed architecture to process queries in parallel for fast results
Data is stored in Capacitor, a proprietary storage format optimized for analytical processing
Top trending discussions
Forecasting problem - Predict daily sku level sales
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
I applied via Recruitment Consultant and was interviewed before Oct 2020. There were 3 interview rounds.
posted on 6 Nov 2015
posted on 29 Nov 2022
I applied via Approached by Company and was interviewed before Nov 2021. There were 2 interview rounds.
Array and OOPS concept. Given set of questions to solve with each oops concept
Inheritance in car model refers to the ability of a new car model to inherit features and characteristics from an existing car model.
Inheritance allows for the creation of a new car model that shares common features with an existing car model
The new car model can add or modify features inherited from the existing car model
For example, a new sports car model can inherit features from a base car model such as engine, tra...
I applied via Naukri.com and was interviewed in Jan 2023. There were 2 interview rounds.
posted on 28 Jun 2022
I applied via Naukri.com and was interviewed in May 2022. There were 2 interview rounds.
Results from pseudo code or steps from an algorithm
Mainly the process of handling issues and development process
I applied via Referral and was interviewed in Sep 2023. There were 3 interview rounds.
Very easy roundand is it a program to implement Parallel Merge Sort and can measure the performance
Coding and decoding reasoning for the shield season is over now I am not sure
posted on 18 Feb 2023
I applied via Recruitment Consulltant and was interviewed in Aug 2022. There were 4 interview rounds.
based on 1 interview
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