SQL and Python interview practice with an AI interviewer
Many data roles test SQL and Python together: query the data, then process it in code, or solve the same problem both ways and explain when you'd use each. Interviewers look for correct code, sensible performance on large data, and the defensive habits (error handling, validation) that keep pipelines running.
In PrepLinx you write and run both SQL and Python in the shared editor. The AI interviewer sees your code and its output, and asks about performance, edge cases and how your solution handles bad data.
What this round tests
SQL
Correct, efficient queries.
Python for data
Clean, readable data processing code.
Integration
Combining SQL and Python sensibly, and knowing which to use for what.
Performance
Handling data that doesn't fit in memory, and avoiding needless passes over it.
Error handling and data quality
Malformed records, retries and validation.
Code quality
Code that's easy to read and test.
Practice questions
Examples of the questions and follow-ups you can expect. Practice saying your answers out loud, not just reading them.
- Parse a log file of JSON events and count sessions per user in Python.
- Write it in SQL, then in Python. When would you choose each?
- Process a 50 GB CSV file on a machine with 8 GB of memory.
- Deduplicate customer records whose emails differ only in case and whitespace.
- Load the results into a table so that retries don't create duplicates.
- Some rows are malformed. How does your code handle them?
How a PrepLinx session works
- 1
Choose the round
Pick the role, the interview type and a difficulty (easy, medium or hard). Optionally add a job profile with your resume and the job description, so the questions relate to that job.
- 2
Interview out loud
The AI interviewer speaks, listens to your answers and asks follow-ups. It doesn't give hints or say whether you're right, just like the real thing. The session runs up to 30 minutes.
- 3
Get scored feedback
A report with a 0–10 score for each dimension the round is graded on, strengths quoted from your own words, weaknesses, concrete improvements and an overall hiring signal, plus the full transcript.
How to prepare
Push work to the database
Filtering and aggregation are usually faster in SQL than after pulling the data into Python.
Stream large data
Use generators, chunks or iterators instead of loading everything into memory.
Handle bad input deliberately
Decide whether to skip, quarantine or fail on malformed rows, and say why.
Test with a tiny example
Run your code on a few hand-made rows before calling it done.
Frequently asked questions
Can I run both SQL and Python?
Yes. Switch the editor's language and run either one during the session. The interviewer sees the code and the output.
How is it scored?
You get 0–10 scores for problem understanding, SQL correctness and efficiency, Python data processing, how you split work between SQL and Python, and robustness and testing, plus communication, with strengths quoted from your own answers and code.
Who is it for?
Data engineers first of all, and analysts or data scientists whose interviews mix SQL and Python.
How long is a session?
Up to 30 minutes.
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Practice your next SQL and Python interview
4 full interviews free, with scored feedback. No credit card, and they don't expire.
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