Take a realistic mock interview with questions and coding tasks prepared by engineers who conduct technical interviews. Get detailed feedback on your strengths and gaps.
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The interview library
Find bugs in Python code, write Python and SQL solutions, and reason through async work and reliable backends.
Available levels
Find bugs in PHP code, write PHP and SQL solutions, and reason through reliable workers and backend services.
Available levels
Work through practical code discussions, write TypeScript and SQL, and explain the decisions behind reliable backends.
Available levels
Work through Go semantics, concurrency, coding tasks, and SQL, then design reliable HTTP services and secure production systems.
Available levels
How it works
Pick a role, sign in with Google, and get ready. Check your microphone or continue in text mode.
Work through questions and coding tasks from a prepared pool. Follow-ups explore your reasoning, and tailored clarifications help you fill in missing details.
Finish with an AI-assisted report: scores by competency, strengths, knowledge gaps, and feedback on each answer.
A look inside the interview
Real interview tasks, with sample answers and evaluation criteria.
Question
What your answer is assessed on
DeepRound
Two customers try to buy the last item at once. How would you keep the order and stock consistent?
Example answer
I’d use one conditional UPDATE to decrease stock only when stock > 0. If no row is updated, I’d return “sold out.” Otherwise, I’d create the order in the same transaction, so both changes commit or roll back together.
DeepRound Follow-up
The order commits, but the response is lost. How would you handle a retry without treating it as a new purchase?
DeepRound vs. ChatGPT
| Included | DeepRound | ChatGPT |
|---|---|---|
| Expert-designed interview scenario | Included with DeepRound | Not included in a general ChatGPT chat |
| Complete 45–50 min interview scenario | Included with DeepRound | Not included in a general ChatGPT chat |
| Role-specific interview structure | Included with DeepRound | Not included in a general ChatGPT chat |
| Structured question-by-question interview flow | Included with DeepRound | Not included in a general ChatGPT chat |
| Purposeful follow-ups for each question | Included with DeepRound | Not included in a general ChatGPT chat |
| Predefined evaluation criteria | Included with DeepRound | Not included in a general ChatGPT chat |
| Consistent scoring across the whole interview | Included with DeepRound | Not included in a general ChatGPT chat |
A real interview structure where every question, follow-up, and evaluation criterion is there for a reason.
Review a sample answer and see what earned points, what was missing, and what to improve.
Completed
Overall score
76/100
Competency breakdown
Strengths
Uses measured plans and checks row estimates after clarification.
Needs attention
Explain how inaccurate row estimates can lead to a poor join or scan choice.
A PostgreSQL query has become slow as the table grows. How would you use EXPLAIN ANALYZE to find the bottleneck and decide what to change?
Databases & data consistency
Your answer
I’d run it on representative data and use actual timings and row counts, not estimated costs, to find expensive scans or sorts. If a scan reads many rows to return a few, I’d consider an index on the filter columns, then compare execution time before and after.
You distinguish measured execution from estimated costs, use actual timings and row counts to locate expensive work, and check whether the change helped.
Your initial answer suggests an index without comparing estimated and actual row counts.
Your clarification adds the missing comparison and a check for outdated statistics. The remaining gap is explaining how inaccurate estimates can lead to a poor join or scan choice.
Clarifying question
You mentioned actual row counts, but not how they compare with the planner’s estimates. What would a large difference tell you, and what would you check before adding an index?
Your clarification
I’d compare estimated and actual rows at each node. If the estimate is 100 rows but the query returns 50,000, I’d check whether statistics are out of date. I’d refresh them with ANALYZE, rerun the plan, and compare the estimates and execution time.
Simple pricing
Choose the package that fits your preparation. Use your attempts with any available interview — they never expire, and there is no subscription.
One-time
1 interview
$9
A complete practice round with detailed feedback.
One-time
3 interviews
$19
Practice, review your feedback, and try again.
Taxes are not included and may be added at checkout.
Interview field notes
A backend interview study map for 2027: where to go deeper in databases, concurrency, reliability, performance, architecture, and security.
A simple purchase query stalls while another transaction stays open. Follow the blocker and see why fast SQL can still mean a slow API.
Diagnose connection pool contention in Go with database/sql metrics, PostgreSQL observations, and a reproducible comparison of larger pools and shorter transactions.
How deep is your round?
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