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π Spring Boot Interview Preparation β Top 30 Questions Preparing for a Java backend interview? These important Spring Boot interview questions will help you revise quickly and boost your confidence. π» π Topics Covered: β Basics of Spring Framework β Annotations & REST APIs β Dependency Injection β Spring Data JPA β Real-time scenario questions π‘ Save this post for quick revision before your interview! Follow π @coding.bytes1 for daily coding content on Java β’ SQL β’ Python β’ DSA #springboot #javadeveloper #java #coding #backenddeveloper

29 MOST ASKED SPRING BOOT ANNOTATION QUESTIONS (Easy β Medium β Hard) Comment "pdf" for interview precise answers to all these questions. Stop randomly preparing. Focus level-wise π π’ EASY (Foundation β must be perfect) 1. What is @SpringBootApplication? 2. What are the components of @SpringBootApplication? 3. What is @Component? 4. Difference between @Component, @Service, @Repository? 5. What is @Autowired? 6. What is @RestController? 7. Difference between @Controller and @RestController? 8. What is @RequestMapping? 9. What is @GetMapping / @PostMapping? 10. What is @PathVariable? π‘ MEDIUM (Real interview level) 11. Constructor vs Field Injection β which is better? 12. What is @Qualifier? 13. What is @Primary? 14. What is @Bean and how is it different from @Component? 15. What is @Configuration? 16. What is @RequestParam vs @RequestBody? 17. What is @ResponseBody? 18. What is @Valid and how does validation work? 19. What is @ExceptionHandler? 20. What is @ControllerAdvice? π΄ HARD (Where most people fail) 21. What is @Transactional and its propagation types? 22. What is @Entity and how mapping works internally? 23. What is @Table? 24. What is @Id and @GeneratedValue strategies? 25. What are @OneToMany and @ManyToOne mappings? 26. FetchType.LAZY vs EAGER β when to use what? 27. What is @Value vs @ConfigurationProperties? 28. What is @EnableAutoConfiguration? 29. How does Spring Boot Auto Configuration work internally? Comment "pdf" for rest of the interview questions and precise answers to all these questions. Save this π and revise before every interview. #systemdesign #engineers #developers #softwareengineering #springboot [coding, system design, springboot, genAl developers, software engineer, coders, java]

@Primary = Default choice @Qualifier = Exact choice β‘ #Programming #Java #SpringBoot #CodingTips #LearnProgramming

This is not a coding question. This is a REST semantics + API contract design problem. βΈ» 1οΈβ£ What most developers think PUT β update full object PATCH β update partial object Simple theory. But internally both may call: save() βΈ» 2οΈβ£ What actually happens in Spring/JPA Flow: API β Controller β Service β repository.save() Inside Hibernate: β if id exists β entity is updated β dirty checking compares fields β only changed fields may be updated in SQL So technically: PUT and PATCH can behave SAME at DB level βΈ» 3οΈβ£ Then what is the real difference? It is NOT save() It is API contract: PUT β full object replacement expected β missing fields = overwrite risk PATCH β partial update allowed β only selected fields change Flow: PUT β full state replacement PATCH β partial state modification βΈ» 4οΈβ£ Why this matters in production PUT misuse: β accidental field overwrite β data loss risk PATCH misuse: β inconsistent partial state if not validated So difference is: β not Hibernate β not save() β but API design rule βΈ» π₯ Interview Ready One-Liner: PUT and PATCH may use the same save() internally, but the real difference is API contractβPUT expects full object replacement, while PATCH allows partial state updates without overwriting entire data. βΈ» π Follow for more such production-level backend breakdowns HAPPY CODING #backend #java #springboot #api #restapi hibernate jpa microservices softwareengineering techindia codingindia indiandeveloper

Check bio for the ebook or comment scenario to get into your Inbox . . . . . . . . . . . . . . . . . . . . #coding #interviewquestions #backenddeveloper #interviewpreparation #springboot

Profiling concept in #springboot #javaprogramming #javaprogrammer #softwaredevelopment #hungrycoders

Spring Boot / Backend Frameworks = Engine of Backend This is where concepts turn into real applications. In this part, weβre learning the core layers behind backend development: β’ Controllers β’ Service Layer β’ Repository Layer β’ Dependency Injection β’ Exception Handling This is where your backend code actually lives. Understand these layers, and Spring Boot architecture starts making sense. Comment βSPRINGβ and Iβll send you the complete notes in DM π© Follow for the Backend Mastery Series π #backend #springboot #java #backenddeveloper #corporatelife softwareengineering coding developers systemdesign programming

Top 100 SpringBoot Questions #java #springboot #softwareengineering #backenddeveloper #dsa [backend developers, frontend developers , full stack developers, mern stack , Spring Boot , Kafka ]

Interviewer: What is the difference between PUT and PATCH in REST APIs? π Most developers answer: PUT β Full Update PATCH β Partial Update Thatβs the textbook definition. But production systems work a little differently. π In Spring Boot + JPA, both requests often end up calling the same method: repository.save() Flow: Request β Controller β Service β Hibernate/JPA β Database Inside Hibernate: β Entity is fetched using ID β Dirty checking detects modified fields β Only changed columns may be updated in SQL Which means: At the database level, PUT and PATCH can behave very similarly. So where is the REAL difference? π The difference is not in Hibernate. The difference is in the API CONTRACT. π’ PUT Represents full resource replacement. Client is expected to send the complete object state. Missing fields may overwrite existing values. Example Risk: If βphoneNumberβ is missing in request, it may become NULL in database. Used when: β Replacing entire resource β Maintaining complete object state π‘ PATCH Represents partial resource modification. Client sends only fields that need changes. Existing fields remain untouched. Used when: β Updating specific fields β Reducing payload size β Avoiding accidental overwrites β Production-Level Concerns Incorrect PUT usage: β Accidental data loss β Null overwrites β Unintended state replacement Incorrect PATCH usage: β Invalid partial states β Broken business validation β Inconsistent resource data π‘ Interview-Ready Answer: βPUT and PATCH may internally use the same save() operation in JPA, but the actual difference lies in REST API semantics. PUT expects full resource replacement, whereas PATCH is designed for partial state modification without replacing the entire object.β Thatβs the difference between: Knowing frameworks β vs Understanding API design semantics β #backend #java #springboot #restapi #api [ hibernate jpa microservices softwareengineering systemdesign backenddevelopment javadeveloper coding developer programming fullstackdeveloper interviewquestions tech softwaredeveloper indiandeveloper]

Git Version Control System - Distributed VCS vs Centralized VCS . . . #softwareengineer #softwaredeveloper #java #systemdesign #springboot

Java Interview Series 50 | Spring Boot Thymeleaf in Spring Boot! A must-know concept for every spring boot interview. π Save this for later ππ» Share to your Java buddy π Follow @abhishek.codelab for more interview prep #coding #java #interview #springboot #javaprogramming
Top Creators
Most active in #spring-boot
Reels Graph Intelligence.
Advanced mapping of high-affinity Instagram Reels semantic patterns identified within the #spring-boot ecosystem.
Strategic Implementation
Our semantic engine has identified these specific pattern clusters as high-affinity matches for #spring-boot. Integrated usage of #spring-boot with strategic Reels tags like #spring and #boots is statistically linked to a significant increase in initial Reels discovery velocity.
In-Depth Hashtag Analysis: #spring-boot
Expert Review β’ June 4, 2026 β’ Based on 12 Reels
Executive Overview
#spring-boot is an actively used Instagram hashtag. Across the 12 trending reels analyzed on this page, the content has accumulated a combined total of 761,565 viewsβ demonstrating healthy engagement activity within this content vertical. The top creator ecosystem features 8 notable accounts, led by @coding_with_deepa with 229,085 total views. The hashtag's semantic network includes 100 related keywords such as #spring, #boots, #springs, indicating its position within a broader content cluster.
Viewership & Reach Analysis
The 12 reels in this dataset have generated a combined 761,565 views, translating to an average of 63,464 views per reel. This strong average viewership suggests healthy algorithmic distribution. Reels using this hashtag are reliably reaching audiences interested in this niche.
The highest-performing reel in this dataset received 229,085 views. This viral outlier performance is 361% of the average reel performance in this set. This significant gap between the top performer and the average highlights the "viral lottery" nature of this hashtag β breakout hits can achieve massive scale.
Content Overview & Top Creators
The #spring-boot ecosystem is dominated by short-form video content (Reels), aligning with Instagram's algorithmic preference for video-first distribution. There are 8 distinct accounts contributing to the trending feed. The top creator, @coding_with_deepa, has contributed 1 reel with a total viewership of 229,085. The top three creators β @coding_with_deepa, @rayofani_, and @navinreddyofficial β together account for 66.7% of the total views in this dataset. The semantic network of #spring-boot extends across 100 related hashtags, including #spring, #boots, #springs, #boote. Creators often use these tags together to reach overlapping audiences.
Discoverability & Reach Potential
The discoverability metrics for #spring-boot indicate an active content ecosystem. The average of 63,464 views per reel demonstrates consistent audience reach. For creators using #spring-boot, posting consistently with trending audio and relevant angles will help you get noticed.
Analyst Verdict
#spring-boot demonstrates the hallmarks of a steadily growing Instagram hashtag. With an average of 63,464 views per reel, the viewership metrics position this hashtag as a reliable reach driver. Creators like @coding_with_deepa and @rayofani_ are leading the charge, setting viewership benchmarks for the community.
Frequently Asked Questions
Everything about #spring-boot on Instagram
Global Reels Trends
Explore high-velocity Instagram Reels hashtags currently shaping global discovery.












