Solidity Interview Questions (2026)
Solidity interview questions test smart contract skill across state, functions, modifiers, events, gas, storage, inheritance, ERC standards, reentrancy, access control, and testing.
45 questions with answersWhat Is Solidity?
Key Takeaways
- ✓Solidity answers includes security and gas reasoning.
- ✓Most rounds cover storage, memory, calldata, modifiers, events, inheritance, ERC-20, ERC-721, reentrancy, and testing.
- ✓Strong candidates use known libraries and explain attack paths.
- ✓Good answers include testnet and audit evidence.
Solidity is a smart contract language for the Ethereum Virtual Machine. Interviews test state, functions, modifiers, events, storage, gas, inheritance, ERC standards, access control, testing, and security bugs.
Watch: Blockchain, Solidity, and Full Stack Web3 Course
Video: Blockchain, Solidity, and Full Stack Web3 Course (freeCodeCamp.org, YouTube)
Test yourself and earn a certificate
6 quick questions. Score 70%+ to download your Solidity certificate.
All Questions on This Page
45 questions- 1. How would you explain storage in a Solidity interview?
- 2. Where does memory matter in real Solidity work?
- 3. What mistake do candidates make with calldata?
- 4. How do you compare modifier with the nearest related idea?
- 5. What does reentrancy prove in real work?
- 6. How would you explain language syntax in a Solidity interview?
- 7. Where does runtime matter in real Solidity work?
- 8. What mistake do candidates make with data types?
- 9. How do you compare control flow with the nearest related idea?
- 10. What does modules prove in real work?
- 11. How would you explain error handling in a Solidity interview?
- 12. Where does debugging matter in real Solidity work?
- 13. What mistake do candidates make with unit testing?
- 14. How do you compare API integration with the nearest related idea?
- 15. What does security checks prove in real work?
- 16. Walk through writing an access-controlled function for Solidity.
- 17. How would you handle writing a small program in a real project?
- 18. What evidence would you collect for debugging a failure?
- 19. What setup is needed before calling an API?
- 20. How do you know handling exceptions worked?
- 21. Walk through validating input for Solidity.
- 22. How would you handle writing a unit test in a real project?
- 23. What evidence would you collect for reviewing code?
- 24. What setup is needed before optimizing a query?
- 25. How do you know deploying a change worked?
- 26. Walk through reading logs for Solidity.
- 27. How would you handle handling authentication in a real project?
- 28. What evidence would you collect for documenting behavior?
- 29. What setup is needed before fixing a defect?
- 30. How do you know planning rollback worked?
- 31. A project runs into reentrancy review finding. What do you check first?
- 32. How would you debug gas cost spikes after storage change without guessing?
- 33. What would make code works in test but fails in production risky in production?
- 34. How would you explain API contract changes in a technical review?
- 35. What trade-off matters most in performance drops after change?
- 36. A project runs into security review blocks release. What do you check first?
- 37. How would you debug unit test misses defect without guessing?
- 38. What would make deployment dependency missing risky in production?
- 39. How would you explain legacy code has no tests in a technical review?
- 40. What trade-off matters most in data type causes bug?
- 41. A project runs into error handling hides issue. What do you check first?
- 42. How would you debug reviewer challenges design without guessing?
- 43. What would make urgent production fix risky in production?
- 44. How would you explain integration timeout in a technical review?
- 45. What trade-off matters most in audit asks for change history?
Solidity Fundamentals
Foundational15 questionsStart here. These are the definitions and first-principle checks that open most rounds.
Q1. How would you explain storage in a Solidity interview?
storage matters in Solidity because it changes data ownership, process control, integration behavior, or production support.
One example from Ethereum smart contract development, DeFi-style logic, token contracts, audits, and testnets needs evidence that proves the behavior works.
For storage, the practical check is whether a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes reflects the intended behavior and whether unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings confirms it.
Watch a deeper explanation
Video: Blockchain, Solidity, and Full Stack Web3 Course (freeCodeCamp.org, YouTube)
Q2. Where does memory matter in real Solidity work?
memory is a platform artifact topic: where it is configured, who owns it, and what breaks if it is wrong.
The artifact is a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes. That keeps the explanation concrete and reviewable.
memory becomes useful when it changes a real choice: safer design, faster execution, clearer ownership, or better failure detection.
Q3. What mistake do candidates make with calldata?
calldata connects business rules to system behavior through the record, transaction, permission, interface, or workflow it affects.
The risk is wrong access, duplicate automation, bad data, broken interface, missed transport, or support noise.
The main risk with calldata is reentrancy, unsafe access control, storage mistakes, integer assumptions, and deployment without audit-grade tests; detection of that risk is part of the technical substance.
Q4. How do you compare modifier with the nearest related idea?
modifier is useful only when tied to a process: actor, data object, approval, report, or integration path.
Validation comes through unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings, not a generic claim that the configuration is done.
modifier connects one concrete artifact, one measurable signal, and one reason the simpler option may not be enough.
| Answer part | What to say | Evidence to mention |
|---|---|---|
| Definition | modifier in one direct sentence. | Official docs or course material |
| Use case | The work where it changes a decision. | Dataset, model, query, dashboard, or pipeline |
| Risk | What breaks when it is misunderstood. | Metric, log, test result, or review note |
Q5. What does reentrancy prove in real work?
reentrancy matters in Solidity because it changes data ownership, process control, integration behavior, or production support.
One example from Ethereum smart contract development, DeFi-style logic, token contracts, audits, and testnets needs evidence that proves the behavior works.
In day-to-day work, reentrancy is judged by the result it protects: correctness, reliability, maintainability, cost, security, or user impact.
Watch a deeper explanation
Video: Salesforce Trailhead Developer Beginner (Salesforce Trailhead, YouTube)
Q6. How would you explain language syntax in a Solidity interview?
language syntax is a platform artifact topic: where it is configured, who owns it, and what breaks if it is wrong.
The artifact is a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes. That keeps the explanation concrete and reviewable.
language syntax has a boundary, behavior inside that boundary, and evidence outside it.
Q7. Where does runtime matter in real Solidity work?
runtime connects business rules to system behavior through the record, transaction, permission, interface, or workflow it affects.
The risk is wrong access, duplicate automation, bad data, broken interface, missed transport, or support noise.
runtime is worth discussing only if it changes an action: what to build, what to test, what to monitor, or what to avoid.
Q8. What mistake do candidates make with data types?
data types is useful only when tied to a process: actor, data object, approval, report, or integration path.
Validation comes through unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings, not a generic claim that the configuration is done.
The useful distinction for data types is where responsibility sits: code, data, configuration, platform, process, or owner.
Q9. How do you compare control flow with the nearest related idea?
control flow matters in Solidity because it changes data ownership, process control, integration behavior, or production support.
One example from Ethereum smart contract development, DeFi-style logic, token contracts, audits, and testnets needs evidence that proves the behavior works.
control flow often fails quietly, so the validation should be observable through unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings.
Q10. What does modules prove in real work?
modules is a platform artifact topic: where it is configured, who owns it, and what breaks if it is wrong.
The artifact is a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes. That keeps the explanation concrete and reviewable.
modules is specific: where it applies, where it does not, and what changes the decision.
Q11. How would you explain error handling in a Solidity interview?
error handling connects business rules to system behavior through the record, transaction, permission, interface, or workflow it affects.
The risk is wrong access, duplicate automation, bad data, broken interface, missed transport, or support noise.
error handling connects theory to delivery when the explanation includes input, output, owner, risk, and proof.
Q12. Where does debugging matter in real Solidity work?
debugging is useful only when tied to a process: actor, data object, approval, report, or integration path.
Validation comes through unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings, not a generic claim that the configuration is done.
debugging goes beyond definition when it includes the operating constraint and verification step.
Q13. What mistake do candidates make with unit testing?
unit testing matters in Solidity because it changes data ownership, process control, integration behavior, or production support.
One example from Ethereum smart contract development, DeFi-style logic, token contracts, audits, and testnets needs evidence that proves the behavior works.
unit testing is tied to the problem it solves, not just the tool or syntax that exposes it.
Watch a deeper explanation
Video: Get Started with SAP HANA Cloud (SAP Developers, YouTube)
Q14. How do you compare API integration with the nearest related idea?
API integration is a platform artifact topic: where it is configured, who owns it, and what breaks if it is wrong.
The artifact is a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes. That keeps the explanation concrete and reviewable.
The decision around API integration should be reversible or at least measurable, especially when reentrancy, unsafe access control, storage mistakes, integer assumptions, and deployment without audit-grade tests is possible.
Q15. What does security checks prove in real work?
security checks connects business rules to system behavior through the record, transaction, permission, interface, or workflow it affects.
The risk is wrong access, duplicate automation, bad data, broken interface, missed transport, or support noise.
security checks needs both the normal path and the edge case that breaks it.
Solidity Practical Interview Questions
Intermediate15 questionsThese questions test whether you can apply the topic to real data, real code, and messy constraints.
Q16. Walk through writing an access-controlled function for Solidity.
For writing an access-controlled function, business process, data owner, environment, test case, and release path before choosing configuration, code, or integration comes first.
writing an access-controlled function maps to a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes: test evidence, data impact, access impact, and release control.
writing an access-controlled function is complete only when the result is visible in unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings and the next owner can repeat the check.
pragma solidity ^0.8.20;
contract Vault {
address public owner;
constructor() { owner = msg.sender; }
modifier onlyOwner() { require(msg.sender == owner, "not owner"); _; }
function sweep(address payable to) external onlyOwner { to.transfer(address(this).balance); }
}Q17. How would you handle writing a small program in a real project?
Handle writing a small program by mapping current behavior, expected behavior, affected records, permission impact, and rollback option.
Delivery judgment covers what to configure, what not to customize, and how to support it after go-live.
The safe path for writing a small program is small scope, known baseline, controlled change, and a rollback or correction option.
Q18. What evidence would you collect for debugging a failure?
Begin debugging a failure in the right environment. Sandbox evidence, test data, and user access checks matter before a production change.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings proves the change. Missing evidence needs a log, report, or test result.
For debugging a failure, the important artifact is a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes; without it, the task is just activity without proof.
Q19. What setup is needed before calling an API?
For calling an API, choose the smallest maintainable change that solves the process need without creating hidden support work.
The owner and rollback path matter because enterprise changes usually touch several teams.
calling an API preserves the user or system outcome first, then optimizes speed, cost, or convenience.
Q20. How do you know handling exceptions worked?
For handling exceptions, business process, data owner, environment, test case, and release path before choosing configuration, code, or integration comes first.
handling exceptions maps to a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes: test evidence, data impact, access impact, and release control.
The risk in handling exceptions is reentrancy, unsafe access control, storage mistakes, integer assumptions, and deployment without audit-grade tests, so the task needs an explicit prevention or detection step.
Q21. Walk through validating input for Solidity.
Handle validating input by mapping current behavior, expected behavior, affected records, permission impact, and rollback option.
Delivery judgment covers what to configure, what not to customize, and how to support it after go-live.
validating input usually touches more than one layer, so separate input, processing, output, and ownership before changing anything.
Q22. How would you handle writing a unit test in a real project?
Begin writing a unit test in the right environment. Sandbox evidence, test data, and user access checks matter before a production change.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings proves the change. Missing evidence needs a log, report, or test result.
writing a unit test stops at a verified result, not a completed command or a passed local run.
Q23. What evidence would you collect for reviewing code?
For reviewing code, choose the smallest maintainable change that solves the process need without creating hidden support work.
The owner and rollback path matter because enterprise changes usually touch several teams.
reviewing code needs a defined expected output, allowed side effects, and evidence source before execution.
Q24. What setup is needed before optimizing a query?
For optimizing a query, business process, data owner, environment, test case, and release path before choosing configuration, code, or integration comes first.
optimizing a query maps to a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes: test evidence, data impact, access impact, and release control.
optimizing a query needs a negative case as well as the happy path, especially when the failure is expensive or hard to see.
Q25. How do you know deploying a change worked?
Handle deploying a change by mapping current behavior, expected behavior, affected records, permission impact, and rollback option.
Delivery judgment covers what to configure, what not to customize, and how to support it after go-live.
The simplest useful version of deploying a change is the one that can be reviewed, repeated, and explained from the evidence.
Watch a deeper explanation
Video: Get Started Building on ServiceNow (ServiceNow Dev Program, YouTube)
Q26. Walk through reading logs for Solidity.
Begin reading logs in the right environment. Sandbox evidence, test data, and user access checks matter before a production change.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings proves the change. Missing evidence needs a log, report, or test result.
For reading logs, document the assumption that matters most because that is where follow-up failures usually start.
Q27. How would you handle handling authentication in a real project?
For handling authentication, choose the smallest maintainable change that solves the process need without creating hidden support work.
The owner and rollback path matter because enterprise changes usually touch several teams.
handling authentication leaves a trace: test result, log line, metric, report, ticket, or review note.
Q28. What evidence would you collect for documenting behavior?
For documenting behavior, business process, data owner, environment, test case, and release path before choosing configuration, code, or integration comes first.
documenting behavior maps to a Solidity contract with state model, access control, events, tests, gas report, deployment plan, and audit notes: test evidence, data impact, access impact, and release control.
The practical choice in documenting behavior is often between a quick local fix and a maintainable change that survives the next release.
Q29. What setup is needed before fixing a defect?
Handle fixing a defect by mapping current behavior, expected behavior, affected records, permission impact, and rollback option.
Delivery judgment covers what to configure, what not to customize, and how to support it after go-live.
fixing a defect becomes reliable when setup, execution, validation, and cleanup are separate and visible.
Q30. How do you know planning rollback worked?
Begin planning rollback in the right environment. Sandbox evidence, test data, and user access checks matter before a production change.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings proves the change. Missing evidence needs a log, report, or test result.
planning rollback controls blast radius by separating what changes now from what stays unchanged.
Solidity Advanced Scenarios
Advanced15 questionsAdvanced rounds test trade-offs, failure modes, and whether the decision can hold up under production pressure.
Q31. A project runs into reentrancy review finding. What do you check first?
For reentrancy review finding, reproduce the issue in the right environment, compare configuration or code, inspect data and permissions, then fix the narrowest failing point.
The practical answer explains user impact, data impact, owner, validation evidence, and how the fix will be monitored.
reentrancy review finding ends with a decision based on unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings, not a guess based on the first symptom.
Q32. How would you debug gas cost spikes after storage change without guessing?
Handle gas cost spikes after storage change by separating process mismatch, data defect, access issue, integration failure, and release mistake before acting.
Prevention includes test script, deployment checklist, access review, reconciliation report, or support handoff note.
The first priority in gas cost spikes after storage change is limiting impact while keeping enough evidence to prove the actual cause.
Q33. What would make code works in test but fails in production risky in production?
Treat code works in test but fails in production as a support incident with business impact: affected users, records, process step, owner, and deadline.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings is the proof source. If it does not prove the issue, say what extra artifact you need.
For code works in test but fails in production, the useful split is symptom, cause, fix, validation, and prevention.
Q34. How would you explain API contract changes in a technical review?
Debug API contract changes by tracing the record or transaction through the platform, integration, report, and audit trail.
The best technical choice avoids risky production guessing and shows a controlled path from defect to verified release.
API contract changes is risky when reentrancy, unsafe access control, storage mistakes, integer assumptions, and deployment without audit-grade tests; the fix should address that risk directly.
Q35. What trade-off matters most in performance drops after change?
For performance drops after change, reproduce the issue in the right environment, compare configuration or code, inspect data and permissions, then fix the narrowest failing point.
The practical answer explains user impact, data impact, owner, validation evidence, and how the fix will be monitored.
The strongest mitigation for performance drops after change is the smallest change that proves or disproves the suspected cause.
Q36. A project runs into security review blocks release. What do you check first?
Handle security review blocks release by separating process mismatch, data defect, access issue, integration failure, and release mistake before acting.
Prevention includes test script, deployment checklist, access review, reconciliation report, or support handoff note.
security review blocks release needs a timeline because order often reveals whether the issue came from data, code, configuration, or process.
Q37. How would you debug unit test misses defect without guessing?
Treat unit test misses defect as a support incident with business impact: affected users, records, process step, owner, and deadline.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings is the proof source. If it does not prove the issue, say what extra artifact you need.
For unit test misses defect, communication matters because the owner, user impact, and next action must be clear before work spreads.
Q38. What would make deployment dependency missing risky in production?
Debug deployment dependency missing by tracing the record or transaction through the platform, integration, report, and audit trail.
The best technical choice avoids risky production guessing and shows a controlled path from defect to verified release.
deployment dependency missing does not widen into a rewrite until the narrow failure has been reproduced and measured.
Q39. How would you explain legacy code has no tests in a technical review?
For legacy code has no tests, reproduce the issue in the right environment, compare configuration or code, inspect data and permissions, then fix the narrowest failing point.
The practical answer explains user impact, data impact, owner, validation evidence, and how the fix will be monitored.
The prevention step for legacy code has no tests is concrete: a test, monitor, rule, review, runbook, or owner change.
Q40. What trade-off matters most in data type causes bug?
Handle data type causes bug by separating process mismatch, data defect, access issue, integration failure, and release mistake before acting.
Prevention includes test script, deployment checklist, access review, reconciliation report, or support handoff note.
For data type causes bug, a rollback is useful only if it restores the failing behavior and has its own validation check.
Q41. A project runs into error handling hides issue. What do you check first?
Treat error handling hides issue as a support incident with business impact: affected users, records, process step, owner, and deadline.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings is the proof source. If it does not prove the issue, say what extra artifact you need.
error handling hides issue is evaluated by blast radius, repeatability, customer impact, and confidence in the evidence.
Q42. How would you debug reviewer challenges design without guessing?
Debug reviewer challenges design by tracing the record or transaction through the platform, integration, report, and audit trail.
The best technical choice avoids risky production guessing and shows a controlled path from defect to verified release.
The best fix for reviewer challenges design is one that reduces recurrence, not just the visible symptom.
Q43. What would make urgent production fix risky in production?
For urgent production fix, reproduce the issue in the right environment, compare configuration or code, inspect data and permissions, then fix the narrowest failing point.
The practical answer explains user impact, data impact, owner, validation evidence, and how the fix will be monitored.
For urgent production fix, the hard part is separating real movement from measurement or environment noise.
Q44. How would you explain integration timeout in a technical review?
Handle integration timeout by separating process mismatch, data defect, access issue, integration failure, and release mistake before acting.
Prevention includes test script, deployment checklist, access review, reconciliation report, or support handoff note.
integration timeout preserves a record of what changed, why it changed, and what proved the change worked.
Q45. What trade-off matters most in audit asks for change history?
Treat audit asks for change history as a support incident with business impact: affected users, records, process step, owner, and deadline.
unit tests, fuzz tests, transaction receipts, gas snapshots, static analysis, and audit findings is the proof source. If it does not prove the issue, say what extra artifact you need.
The final check for audit asks for change history is whether the same failure can be caught earlier next time.
Solidity vs Related Interview Topics
Solidity overlaps with nearby topics, but each topic has a specific center of gravity. The table separates tool knowledge from judgment.
| Area | What it checks | Interview signal | Common miss |
|---|---|---|---|
| Solidity | Smart contract correctness, gas, and security | Can write contracts that protect funds and state | Deploying before threat modeling and tests |
| Configuration | How the platform is shaped without code | Can solve with standard features first | Coding around simple settings |
| Integration | How data enters and leaves | Can protect contracts and errors | Ignoring retries and ownership |
| Release | How change reaches users | Can test, deploy, and rollback | Changing production without evidence |
Solidity interview scoring weight
The exact mix depends on role level and company stack.
Scale: Hyring editorial score for interview preparation, not an external benchmark.
- Concepts: platform basics
- Process: business fit
- Integration: data flow
- Release: change control
How to Prepare for a Solidity Interview
Prepare Solidity by tying each term to a business process, a platform artifact, a test case, and a production support signal.
- One business process example and explain where the platform stores, routes, and validates data is useful.
- Know the difference between configuration, customization, integration, and release work.
- Practice a defect story with root cause, fix, test evidence, and rollback option.
- Use official product docs for feature names so your wording matches real projects.
Solidity interview prep flow
Strong answers definitions connects to a real project decision.
What Strong Solidity Answers Prove
Strong Solidity answers show platform fluency and delivery judgment. the key point is how you turn business rules into working, tested, supportable change.
| Area | Weak answer | Strong answer |
|---|---|---|
| Process | Talks only about screens. | Maps actors, records, statuses, and approvals. |
| Platform fit | Builds custom work first. | Uses standard capability unless a real gap exists. |
| Integration | Says data syncs somehow. | Names source, target, contract, error handling, and owner. |
| Release | Assumes deploy means done. | Covers test data, rollback, monitoring, and support handoff. |
Solidity evidence path
This path fits answers that need proof, not just a definition.
Test Yourself: Solidity Quiz
Ready to test your Solidity knowledge?
6 questions, about 4 minutes. Score 70% or higher to earn a shareable certificate.
Frequently Asked Questions
What do Solidity interviews usually ask?
What should I prepare first for Solidity?
What project should I discuss for Solidity?
What is the biggest Solidity interview mistake?
What makes Solidity coverage complete?
How should I use this Solidity question bank before a technical screen?
Practice enterprise platform answers with feedback
Hyring's AI Video Interviewer helps you practice enterprise platform answers with examples, trade-offs, and follow-up reasoning.
Try AI interview prepSources
Written by Adithyan RK