Hestabit Technologies MIS Executive Interview Questions and Answers

Q1. Normal question about past experience
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Q2. When you can join and all
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SQL test on Select query, insert, create

Q1. What are the main principles of the Java
Ans. 
Main principles of Java include object-oriented programming, platform independence, and automatic memory management.
• Object-oriented programming: Java is based on classes and objects, allowing for encapsulation, inheritance, and polymorphism.

• Platform independence: Java code can run on any platform that has a Java Virtual Machine (JVM) installed.

• Automatic memory management: Java uses garbage collection to automatically m
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Q2. What is the different between == and equals method
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The == operator compares the memory addresses of two objects, while the equals method compares the content of two objects.
• The == operator is used to compare the memory addresses of two objects in Java.

• The equals method is used to compare the content of two objects in Java.

• Example: String str1 = new String("hello"); String str2 = new String("hello"); str1 == str2 will return false, but str1.equals(str2) will return true
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Q1. What is jdk, string related questions, basic java oops questions?
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Normal apptitude queations were asked in this round

Q1. Questions on javascript Basic of HTML, CSS
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Q1. Spring boot related questions
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Q2. Java related questios
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
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 ...read more
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Q2. Parametric vs non parametruc model
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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...read more
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