Narasimhulu Maddireddy

Java Interview Questions & Answers – Ace Your Next Interview! Preparing for a Java developer role? This collection of frequently asked Java interview questions and answers is designed to help you build clarity and confidence. It covers core concepts like OOP, exception handling, multithreading, collections, JVM, and more – all the essential topics interviewers love to ask. Ideal for freshers and professionals aiming to crack technical interviews with ease. 🔁 Repost & Share with others to help them upskill! 📢 Follow Sakshi Singh Kushwaha for more such content. #Java #JavaInterview #InterviewPreparation #OOP #CoreJava

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🚀 Master Java Fundamentals with 50 Must-Know MCQs! 🚀 I’ve compiled a set of 50 carefully selected Java basics interview questions with answers – perfect for students, freshers, and anyone preparing for technical interviews. This resource covers everything from syntax, variables, data types, operators, control flow, OOP basics, JVM/JRE/JDK, memory management, and more – all in a simple Q&A format. 📘 Download the PDF and strengthen your core Java knowledge today! #Java #Programming #InterviewPreparation #JavaDeveloper #Coding #MCQs #JavaInterview #SoftwareEngineering #CareerGrowth

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🚨 Java Interview Questions (2025) 🚨 1. Explain the difference between volatile, synchronized, and Atomic variables in Java. 2. How does the Java Memory Model (JMM) handle visibility and ordering of threads? 3. What is the difference between CompletableFuture and traditional Future in Java? 4. How do you implement a custom annotation in Java and process it at runtime? 5. Explain the difference between checked, unchecked, and error types in Java. 6. How does Garbage Collection work in Java 17+ (including G1 GC and ZGC)? 7. What are memory leaks in Java, and how do you detect and fix them? 8. How does Optional help in avoiding NullPointerException? 9. How do you implement caching in Spring Boot (with @Cacheable, @CacheEvict)? 10. What is the difference between BeanFactory and ApplicationContext? 11. Explain the Bean lifecycle in Spring (with example). 12. What are @Lazy and @DependsOn annotations in Spring, and when to use them? 13. How does Spring Boot auto-configuration work internally? 14. What is the role of spring.factories in Spring Boot? 15. What is the difference between application․properties and application.yml in Spring Boot? 16. How do you implement transaction management in Spring Boot (declarative vs programmatic)? 17. Explain the difference between @Transactional propagation types in Spring. 18. What is the difference between JpaRepository and CrudRepository in Spring Data JPA? 19. How do you handle pagination and sorting in Spring Data JPA? 20. How to implement a custom query in Spring Data JPA using @Query annotation? 21. Explain how to implement exception handling in REST APIs using @ControllerAdvice and @ExceptionHandler. 22. What is the difference between @RestController and @Controller in Spring? 23. How do you secure a REST API using Spring Security and JWT? 24. What is the difference between Kafka’s producer and consumer configurations in a Spring Boot microservice? 25. How do you design an event-driven microservice architecture using Spring Boot, Kafka, and REST APIs? If you like my post, please leave a comment. #Java #AdvancedJava #JavaDeveloper #SpringBoot #SpringFramework #Microservices #InterviewPrep

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Solve this this cheatsheet with 8 Dynamic Programming patterns and 24 LeetCode problems if you're having trouble with DP. Learning to recognize these patterns will help you solve 90% of any DP problems you may come across in coding interviews 1 // Basic Dynamic Programming →When to use: Use when you notice recursive solutions with overlapping subproblems and repeated calculations. Storing solutions to subproblems (memoization or tabulation) leads to major efficiency gains. →Where: Problems with redundant computations in recursion; optimal to store intermediate results. ∟Practice Problems: →Fibonacci Number →Climbing Stairs →House Robber 2 // Optimal Substructure →When to use: Use when the optimal solution can be built by combining optimal solutions to its subproblems. →Where: Problems where “breaking down” a big problem into smaller, similar problems leads to the overall solution. ∟Practice Problems: →Maximum Subarray →Longest Increasing Subsequence →Coin Change 3 // Interval/Range DP →When to use: Use when the problem asks you to solve for intervals or ranges, and the answer for a range depends on its subranges. →Where: Problems that involve merging, splitting, or making choices over intervals or subarrays. ∟Practice Problems: →Minimum Falling Path Sum →Burst Balloons →Strange Printer 4 // Knapsack Problems →When to use: Use when you need to select items with weights/values to maximize/minimize some total without exceeding a limit. →Where: Problems with choices under constraints (like weight, capacity, budget). ∟Practice Problems: →0/1 Knapsack →Partition Equal Subset Sum →Target Sum 5 // Prefix Sums →When to use: Use when you need to repeatedly compute sums or counts over subarrays/ranges, and want fast queries after some preprocessing. →Where: Problems that ask for sum, count, or aggregate in O(1) time after setup. ∟Practice Problems: →Subarray Sum Equals K →Range Sum Query - Immutable →Count of Range Sum 6 // Counting Problems →When to use: Use when the goal is to count the number of ways to reach a result, subject to certain transitions or rules. →Where: Problems about counting combinations, arrangements, or ways to reach a state. ∟Practice Problems: →Unique Paths III →Distinct Subsequences →Count Numbers with Unique Digits 2 more patterns in the comments -- Also, If you want to solve problems fast and with solid accuracy, you need to think in patterns. Without patterns, you can solve every problem in an interview that you’ve seen once, but once you see a new problem, you will get stuck. Check out my DSA pattern product, it completely changes the way you prepare. Link: https://lnkd.in/g94khGEh - help you track and learn the clues and triggers of different patterns - provide AI-based doubt solving + space to add your own notes - solve 250+ good problems with 5 problems each on 50 patterns

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🧩 Top 150 DSA Interview Questions – Company Based 🚀 Preparing for FAANG & top product-based companies? Most interviews are built around Data Structures & Algorithms (DSA). Here’s a curated list of company-specific questions you must practice 👇 🔹 Companies & Focus Areas 📌 Amazon – Arrays, Strings, HashMaps, Dynamic Programming 📌 Apple – Trees, Graphs, Recursion, System-Oriented Problems 📌 Netflix – Scalability + Advanced DSA (Heap, Graphs, Design) 📌 Google – Dynamic Programming, Graphs, Backtracking 📌 Microsoft – Linked Lists, Bit Manipulation, Binary Trees 📌 Meta – Arrays, Hashing, System Design + DSA mix 🔹 Example Questions ✅ Find the longest substring without repeating characters ✅ Implement LRU Cache ✅ Detect cycle in a linked list ✅ Find kth largest element in an array ✅ Minimum number of platforms needed (Greedy) ✅ Word Ladder (Graph + BFS) ✅ Trapping Rainwater (Two pointers) ✅ Serialize & Deserialize a Binary Tree 📘 Free DSA Resources 🔗 LeetCode DSA Patterns – https://lnkd.in/dTQ7kUqR 🔗 GeeksforGeeks 150 Questions – https://lnkd.in/dGm9ShP9 🔗 Striver’s DSA Sheet – https://lnkd.in/gbefNruz 🔗 InterviewBit DSA – https://lnkd.in/gwR3SJJW 🎯 Interview Prep Tips ✔️ Don’t just solve – understand patterns. ✔️ Time & Space complexity = always explain. ✔️ Mock interviews help boost confidence. ✔️ For freshers → focus on arrays, strings, recursion. ✔️ For jobs → go deeper into DP, graphs, system-oriented DSA. ✍️ Susmitha Chakrala | Resume & LinkedIn Optimization Expert Helping freshers & professionals crack DSA + FAANG-level interviews with: 📄 ATS-Friendly Resumes | 🔗 LinkedIn Makeovers | ✉️ Interview Coaching 📩 DM me for resume help before applying to FAANG & top tech jobs 🚀 🔖 #DSA #CodingInterview #Amazon #Netflix #Apple #FAANG #SoftwareEngineering #TechCareers #Internships

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A useful insight for SDETs preparing for Coding interviews 1) Problem: Move negative numbers to left (start of the input array) Here's a quick solution for the problem: int index = 0; for(int i = 0; i<nums.length; i++) { if(nums[i] < 0) { if(nums[index] > 0) { int temp = nums[index]; nums[index++] = nums[i]; nums[i] = temp; } } } System.out.println(Arrays.toString(nums)); 2) Problem: Move negative numbers to right (end of the input array) Here's a quick solution for the problem: int index = nums.length-1; for(int i = nums.length-1; i>=0; i--) { if(nums[i] < 0) { int temp = nums[index]; nums[index] = nums[i]; nums[i] = temp; index--; } } System.out.println(Arrays.toString(nums)); -x-x- If you read the solution carefully, its a opposite copy. Which is called as algorithmic duality. To learn more such concepts, Dive into my latest course for SDET Coding interview prep: https://lnkd.in/ggXcYU2s #japneetsachdeva

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𝐏𝐚𝐧𝐝𝐚𝐬 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🐼 🔴 𝐃𝐚𝐭𝐚 𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐜𝐬𝐯(): Read CSV files. ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐭𝐚𝐛𝐥𝐞(): Read delimited files like TSV. ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐞𝐱𝐜𝐞𝐥(): Read Excel files. ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐬𝐪𝐥(): Read SQL queries or database tables. ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐣𝐬𝐨𝐧(): Read JSON data. ◾ 𝐩𝐝.𝐫𝐞𝐚𝐝_𝐡𝐭𝐦𝐥(): Parse HTML tables into DataFrames. ◾ 𝐩𝐝.𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞(): Create DataFrames manually. ◾ 𝐩𝐝.𝐜𝐨𝐧𝐜𝐚𝐭(): Combine DataFrames. ◾ 𝐩𝐝.𝐬𝐞𝐫𝐢𝐞𝐬(): Create Pandas Series objects. ◾ 𝐩𝐝.𝐝𝐚𝐭𝐞_𝐫𝐚𝐧𝐠𝐞(): Generate date ranges. 🔴 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 ◾ 𝐩𝐝.𝐟𝐢𝐥𝐥𝐧𝐚(): Fill missing values. ◾ 𝐩𝐝.𝐝𝐫𝐨𝐩𝐧𝐚(): Remove missing values. ◾ 𝐩𝐝.𝐬𝐨𝐫𝐭_𝐯𝐚𝐥𝐮𝐞𝐬(): Sort DataFrame by column values. ◾ 𝐩𝐝.𝐚𝐩𝐩𝐥𝐲(): Apply functions along axis. ◾ 𝐩𝐝.𝐠𝐫𝐨𝐮𝐩𝐛𝐲(): Group data for aggregation or transformation. ◾ 𝐩𝐝.𝐚𝐩𝐩𝐞𝐧𝐝(): Append rows to DataFrames. ◾ 𝐩𝐝.𝐣𝐨𝐢𝐧(): Merge DataFrames on indexes. ◾ 𝐩𝐝.𝐫𝐞𝐧𝐚𝐦𝐞(): Rename columns or indexes. ◾ 𝐩𝐝.𝐬𝐞𝐭_𝐢𝐧𝐝𝐞𝐱(): Set DataFrame index. ◾ 𝐩𝐝.𝐭𝐨_𝐜𝐬𝐯(): Save DataFrame to CSV. 🔴 𝐃𝐚𝐭𝐚 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 ◾ 𝐩𝐝.𝐡𝐞𝐚𝐝(), 𝐩𝐝.𝐭𝐚𝐢𝐥(): View rows from the start or end. ◾ 𝐩𝐝.𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞(): Summarize numerical data. ◾ 𝐩𝐝.𝐢𝐧𝐟𝐨(): Display DataFrame information. ◾ 𝐩𝐝.𝐦𝐞𝐚𝐧(), 𝐩𝐝.𝐦𝐞𝐝𝐢𝐚𝐧(): Compute mean and median. ◾ 𝐩𝐝.𝐜𝐨𝐮𝐧𝐭(): Count non-NA values. ◾ 𝐩𝐝.𝐬𝐭𝐝(): Compute standard deviation. ◾ 𝐩𝐝.𝐦𝐚𝐱(), 𝐩𝐝.𝐦𝐢𝐧(): Find maximum and minimum values. You can easily clear Pandas Functions with the help of this post. ⏩ Join to learn Data Science& Analytics:- https://t.me/LK_Data_world 💬 If you found this PDF useful, like, save, and repost it to help others in the community! 🔄 📢 Connect with Lovee Kumar 🔔 for more content on Data Engineering, Analytics, and Big Data #data #dataanalytics #dataengineer #dataanalysis #datascience #skills #python #pythonlearning #numpy #guide #pandas #linkedinconnectio

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