Level 01
Junior
Explain everyday Python behavior and solve introductory Python and SQL tasks.
About 55 minutes
Interview focus
Core fundamentals
All interviews
Python backend mock interview
A Python interview built question by question.
Find bugs in realistic Python code, write Python and SQL solutions, and practice the decisions behind reliable async workflows and backend services.
Designed by interviewers. Adapted to your answers. Scored against prepared criteria.
About 55–60 minutes 3 levels Detailed report
Choose your level
A round calibrated for where you are now.
Rounds take about 55–60 minutes. The focus shifts from clear fundamentals to deeper production trade-offs.
Start Junior interview
Level 02
Middle
Work through caching, retries, transactions, and practical Python and SQL.
About 60 minutes
Interview focus
Practical application
Level 03
Senior
Explore async lifecycles, bounded resources, and failure recovery through code discussions, Python, and SQL.
About 60 minutes
Interview focus
Production depth
What the round covers
The skills behind production Python backends.
Your selected level shifts the emphasis from fundamentals toward deeper production trade-offs.
Calibrated
to your selected level
Role-specific
coverage grounded in backend work
Adaptive
follow-ups shaped by your answers
Python language & runtime
Types, mutable state, async lifecycles, and memory behavior.
Python coding
Write and fix Python code for data processing, caching, and async work.
Application architecture
Boundaries, dependencies, responsibilities, and provider integrations.
Databases & data consistency
Transactions, race conditions, indexing, query behavior, and ORM pitfalls.
SQL coding
Write PostgreSQL queries for filtering, aggregation, joins, and data analysis.
Queues & background processing
Worker design, retries, idempotency, acknowledgements, and failure handling.
Web application security
Authorization, injection, XSS, CSRF, secrets, tokens, and safe uploads.
DeepRound vs. ChatGPT
A complete Python interview designed for your level.
| 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 Python interview structure where every question, follow-up, and evaluation criterion is there for a reason.
See what’s behind every score
Review a sample answer and see what earned points, what was missing, and what to improve.
Completed
Overall score
76/100
Competency breakdown
Python language & runtime82
Python coding76
Application architecture74
Databases & data consistency72
SQL coding76
Queues & background processing75
Web application security75
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.
Evidence behind the score
1 sample answer0194.5/100
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.
Actual execution100/100
You distinguish measured execution from estimated costs, use actual timings and row counts to locate expensive work, and check whether the change helped.
Plan diagnosisBefore clarification: 50; after clarification: 90/100
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.
See how clarification improved Plan diagnosis
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.
The full practice loop
About 55–60 minutes. Three useful outcomes.
- 01
Explain decisions and write code
Answer discussion questions out loud or in text, and write your solutions to coding tasks in the editor.
- 02
Go one level deeper
A prepared follow-up or focused clarification tests the reasoning behind your first answer.
- 03
Turn gaps into a study plan
Use the competency breakdown and answer-level feedback to decide what to revisit next.
Find the gaps before the real interview does.
Practice explaining the decisions that matter in a real Python backend interview.