Visa Interview Questions, Process, and Tips

Q1. String related question
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Q2. Design job portal
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Q1. Were related to java, dsa and aptitude
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Q2. Spring , spring boot, dsa med- high
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Q1. DSA round LLD+HLD
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Q1. DSA round and project discussion
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So total 4 coding questions 2-medium 2-hard

Q1. One-on-one round more conditional questions 4-5 technical questions
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Q2. 1os question 1dbms 1java 1cpp 1 computer network que and more situational questions
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Q3. Diff between rdbms dbms
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RDBMS is a type of DBMS that stores data in a structured format with relationships between tables.
• RDBMS enforces ACID properties for database transactions.

• RDBMS uses SQL for querying and managing data.

• DBMS is a general term for any system that manages databases, while RDBMS is a specific type.

• DBMS may not support relationships between tables like RDBMS does.
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Q4. Opps concept in java
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Oops concept in Java refers to Object-Oriented Programming principles like Inheritance, Encapsulation, Polymorphism, and Abstraction.
• Inheritance allows a class to inherit properties and behavior from another class.

• Encapsulation involves bundling data and methods that operate on the data into a single unit.

• Polymorphism allows objects to be treated as instances of their parent class.

• Abstraction hides the implementation d...read more
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Q5. Sorting algorithms in any language
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Sorting algorithms are used to arrange elements in a specific order in an array.
• Common sorting algorithms include Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, and Heap Sort.

• Each algorithm has its own time complexity and best/worst case scenarios.

• For example, Merge Sort has a time complexity of O(n log n) and is efficient for large datasets.
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Q1. Why are you interested in this role
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Q2. Provide an example how you solved a problem
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I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.
• Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.

• Preprocessed and cleaned the data to handle missing values and outliers.

• Built and trained a machine learning model using algorithms like logistic regression and random forest.

• Evaluated the model's perform...read more
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