Django Interview Questions to Ask Before You Hire

12 Django interview questions — with what to listen for

1. 1

   Explain the N+1 query problem in Django and how you detect and fix it.

   What a strong answer covers

   Lazy related-object access firing a query per row; fixes via select_related for foreign keys and prefetch_related for many-to-many and reverse relations. Detection matters as much: django-debug-toolbar, query logging, or assertNumQueries in tests. This is the single best Django screening question.

2. 2

   When does the ORM stop being enough for you, and what do you reach for then?

   What a strong answer covers

   Annotations, subqueries, and window expressions before dropping lower; then raw SQL or database views for genuinely complex analytics, wrapped and tested. The wrong answers are both extremes: raw SQL everywhere, or ORM loyalty that grinds through impossible queries.

3. 3

   Walk me through a risky migration — say, adding a non-nullable column to a large, busy table — without downtime.

   What a strong answer covers

   Multi-step deploys: add nullable with default handling, backfill in batches, then enforce the constraint; awareness of table locks, of separating schema and data migrations, and of keeping migrations reversible where possible. Anyone who says 'just makemigrations and deploy' has not run Django at scale.

4. 4

   How do Django's transaction controls work, and where do you put transaction boundaries in a request?

   What a strong answer covers

   atomic as decorator or context manager, ATOMIC_REQUESTS trade-offs, savepoints from nesting, and on_commit for side effects like emails and task enqueues so they do not fire on a rolled-back transaction. The on_commit detail is a reliable seniority marker.

5. 5

   What security protections does Django give you for free, and where can you still shoot yourself in the foot?

   What a strong answer covers

   CSRF tokens, ORM parameterization against SQL injection, template autoescaping, clickjacking middleware, password hashing. Foot-guns: mark_safe and safe filters, raw queries with string formatting, DEBUG=True in production, permissive CORS, and missing object-level permission checks.

6. 6

   In Django REST Framework, how do you keep serializers from becoming a performance problem?

   What a strong answer covers

   Queryset optimization matched to serializer fields, avoiding per-object queries in SerializerMethodField, pagination as standard, and splitting list versus detail serializers. Mentioning profiling before optimizing is the right instinct.

7. 7

   How do you handle long-running work — report generation, image processing, external API calls — in a Django app?

   What a strong answer covers

   Task queues, typically Celery or lighter alternatives, with a broker, retry policy, idempotent task design, and result handling. Enqueue-after-commit and monitoring of queue depth show production experience beyond the tutorial.

8. 8

   Where does caching fit in a Django application, and what has bitten you?

   What a strong answer covers

   Layers: per-view, template fragment, and low-level cache API against Redis or similar, plus queryset caching pitfalls. The bite stories that ring true involve invalidation — stale permission checks, cached user-specific data leaking across sessions.

9. 9

   How do you structure a Django project as it grows past a handful of apps?

   What a strong answer covers

   Apps around business domains with clear boundaries, service or selector layers when views and models get fat, settings split per environment, and restraint about premature microservices. Hearing 'fat models, thin views, and then services when models get fat too' in some form is fine orthodoxy.

10. 10

    How do you test a Django application, and what do you deliberately not test?

    What a strong answer covers

    pytest-django as the common choice, factories over fixtures, separate fast unit tests from database-hitting tests, API tests through the test client, and skipping framework behavior itself. assertNumQueries for regression-proofing hot paths is a strong bonus.

11. 11

    A Django view is slow in production but fast locally. Take me through your investigation.

    What a strong answer covers

    Production-shaped data volume as the first suspect, then query plans, missing indexes, N+1s that small local datasets hide, external calls inside the request cycle, and APM or slow-query logs for evidence. The dataset-size instinct is the key tell.

12. 12

    When would you choose async views or channels in Django, and when is sync plus a queue the better answer?

    What a strong answer covers

    Async for high-concurrency I/O-bound endpoints and websockets via Channels; honest acknowledgment that most Django apps do fine synchronous with workers for slow tasks, and that mixing sync ORM calls into async paths blocks the event loop. Measured adoption over fashion.

Frequently asked questions