Addverb Technologies Graphic Designer Interview Questions and Answers

Given programming questions
String programs

Q1. Asked basic to intermediate level questions Datatypes OOP concepts Code snippets
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Q1. Java spring boot
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Q2. Angular routing,
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2-3 basic dsa questions

Q1. Basic dsaquestions basic dsa
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Q2. Basic dsa questions basic dsa
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Q1. System architecture
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Q2. Dict ,list and other questions
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It was a combination of Maths and Electronics basics.

Q1. Introduce yourself?
Ans. 
I am a recent graduate with a degree in Computer Science and a passion for coding and problem-solving.
• Recent graduate with a degree in Computer Science

• Passionate about coding and problem-solving

• Strong communication and teamwork skills
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Q2. Tell me about your projects?
Ans. 
I have worked on various projects including a web development project, a data analysis project, and a machine learning project.
• Developed a website for a local business using HTML, CSS, and JavaScript.

• Conducted data analysis on customer feedback data to identify trends and make recommendations for improvement.

• Implemented a machine learning algorithm to predict customer churn for a telecom company.
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Q1. Mu age 17 years old
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Q2. What was the wrong
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Q3. I read class 10
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Q4. I am study to cbse board
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Q5. I am busy person
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Q1. Good URI design
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Q2. Accessing id parameter in URL
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Accessing id parameter in URL involves parsing the URL and extracting the id value.
• Use the window.location object to access the URL of the current page.

• Parse the URL using methods like split() or URLSearchParams to extract the id parameter.

• Example: If the URL is 'http://example.com/page?id=123', you can access the id value by parsing the URL.
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Q1. Salary expectations
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Q2. Plan of growing up technically
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Q1. 1. Basic OOP Concepts 2. Difference between structure and class in terms of constructor 3. Can members of structure be defined as private 4. What's lambda function and it's real life use case 5. Difference...read more
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Q2. 6. What's O(n) in time and space complexity
Ans. 
O(n) refers to linear time and space complexity.
• O(n) means that the time or space required by an algorithm grows linearly with the size of the input.

• It implies that the algorithm's performance is directly proportional to the input size.

• For example, if an algorithm takes 10 seconds to process 100 elements, it would take 20 seconds to process 200 elements.

• O(n) complexity is considered efficient and desirable in most case
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