Typical java interview questions sorted by experience


Junior
* Name some of the characteristics of OO programming languages
* What are the access modifiers you know? What does each one do?
* What is the difference between overriding and overloading a method in Java?
* What’s the difference between an Interface and an abstract class?
* Can an Interface extend another Interface?
* What does the static word mean in Java?
* Can a static method be overridden in Java?
* What is Polymorphism? What about Inheritance?
* Can a constructor be inherited?
* Do objects get passed by reference or value in Java? Elaborate on that.
* What’s the difference between using == and .equals on a string?
* What is the hashCode() and equals() used for?
* What does the interface Serializable do? What about Parcelable in Android?
* Why are Array and ArrayList different? When would you use each?
* What’s the difference between an Integer and int?
* What is a ThreadPool? Is it better than using several “simple” threads?
* What the difference between local, instance and class variables?

Mid
* What is reflection?
* What is dependency injection? Can you name a few libraries? (Have you used any?)
* What are strong, soft and weak references in Java?
* What does the keyword synchronized mean?
* Can you have “memory leaks” on Java?
* Do you need to set references to null on Java/Android?
* What does it means to say that a String is immutable?
* What are transient and volatile modifiers?
* What is the finalize() method?
* How does the try{} finally{} works?
* What is the difference between instantiation and initialisation of an object?
* When is a static block run?
* Why are Generics are used in Java?
* Can you mention the design patterns you know? Which of those do you normally use?
* Can you mention some types of testing you know?

Senior
* How does Integer.parseInt() works?
* Do you know what is the “double check locking” problem?
* Do you know the difference between StringBuffer and StringBuilder?
* How is a StringBuilder implemented to avoid the immutable string allocation problem?
* What does Class.forName method do?
* What is Autoboxing and Unboxing?
* What’s the difference between an Enumeration and an Iterator?
* What is the difference between fail-fast and fail safe in Java?
* What is PermGen in Java?
* What is a Java priority queue?
* *s performance influenced by using the same number in different types: Int, Double and Float?
* What is the Java Heap?
* What is daemon thread?
* Can a dead thread be restarted?

Source: medium.

🚀 Coding Projects & Ideas 💻

Inspire your next portfolio project — from beginner to pro!

🏗️ Beginner-Friendly Projects

1️⃣ To-Do List App – Create tasks, mark as done, store in browser.
2️⃣ Weather App – Fetch live weather data using a public API.
3️⃣ Unit Converter – Convert currencies, length, or weight.
4️⃣ Personal Portfolio Website – Showcase skills, projects & resume.
5️⃣ Calculator App – Build a clean UI for basic math operations.

⚙️ Intermediate Projects

6️⃣ Chatbot with AI – Use NLP libraries to answer user queries.
7️⃣ Stock Market Tracker – Real-time graphs & stock performance.
8️⃣ Expense Tracker – Manage budgets & visualize spending.
9️⃣ Image Classifier (ML) – Classify objects using pre-trained models.
🔟 E-Commerce Website – Product catalog, cart, payment gateway.

🚀 Advanced Projects

1️⃣1️⃣ Blockchain Voting System – Decentralized & tamper-proof elections.
1️⃣2️⃣ Social Media Analytics Dashboard – Analyze engagement, reach & sentiment.
1️⃣3️⃣ AI Code Assistant – Suggest code improvements or detect bugs.
1️⃣4️⃣ IoT Smart Home App – Control devices using sensors and Raspberry Pi.
1️⃣5️⃣ AR/VR Simulation – Build immersive learning or game experiences.

💡 Tip: Build in public. Share your process on GitHub, LinkedIn & Twitter.

🔥 React ❤️ for more project ideas!

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

Join @free4unow_backup for more free resources.

ENJOY LEARNING👍👍

How to Learn Java in 2025

1. Set Clear Goals:
   - Define your learning objectives. Do you want to build web applications, mobile apps, or work on enterprise-level software?


2. Choose a Structured Learning Path:
   - Follow a structured learning path that covers the fundamentals of Java, object-oriented programming principles, and essential libraries.


3. Start with the Basics:
   - Begin with the core concepts of Java, such as variables, data types, operators, and control flow statements.


4. Master Object-Oriented Programming:
   - Learn about classes, objects, inheritance, polymorphism, and encapsulation.


5. Explore Java Libraries:
   - Familiarize yourself with commonly used Java libraries, such as those for input/output, networking, and data structures.


6. Practice Regularly:
   - Write code regularly to reinforce your understanding and identify areas where you need more practice.


7. Leverage Online Resources:
   - Utilize online courses, tutorials, and documentation to supplement your learning.


8. Join a Coding Community:
   - Engage with online coding communities and forums to ask questions, share knowledge, and collaborate on projects.


9. Build Projects:
   - Create simple projects to apply your skills and gain practical experience.


10. Stay Updated with Java Releases:
    - Keep up with the latest Java releases and updates to ensure your knowledge remains current.


11. Explore Frameworks and Tools:
    - Learn about popular Java frameworks and tools, such as Spring Boot, Maven, and IntelliJ IDEA.


12. Contribute to Open Source Projects:
    - Contribute to open source Java projects to gain real-world experience and showcase your skills.


13. Seek Feedback and Mentoring:
    - Seek feedback from experienced Java developers and consider mentorship opportunities to accelerate your learning.


14. Prepare for Certifications:
    - Consider pursuing Java certifications, such as the Oracle Certified Java Programmer (OCJP), to validate your skills.


15. Network with Java Developers:
    - Attend Java meetups, conferences, and online events to connect with other Java developers and learn from their experiences.