HardTechnical screen30 min to answer

Represent a one-nearest-neighbor classifier using a feed-forward neural network.

Asked at

OpenAI

1Give yourself 30 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify objective, users, constraints, and failure tolerance.
02Cover distance computation, network construction, argmin approximation, limitations in a causal structure.
03Compare at least two credible approaches.
04Specify evaluation, rollout, monitoring, and rollback.

Reference answer

strong answer

I would begin by defining the target outcome and acceptable failure boundary. For ML theory, I would analyze distance computation, network construction, argmin approximation, limitations, compare alternatives on quality, latency, cost, and risk, and validate on representative slices before a monitored rollout.

distance computation
network construction
argmin approximation
limitations

Requires scenario-specific numbers
Should include relevant personal experience

alternative answer

I would start with the riskiest assumption in ML theory, create the cheapest discriminating experiment, then use the evidence to choose an architecture and release threshold.

experimentation
risk-first sequencing
decision clarity

Needs deeper implementation detail

Grade yourself

distance computation

weight 30%

StrongExplains distance computation with mechanisms, trade-offs, and validation.

WeakNames distance computation without operational implications.

network construction

weight 25%

StrongExplains network construction with mechanisms, trade-offs, and validation.

WeakNames network construction without operational implications.

argmin approximation

weight 25%

StrongExplains argmin approximation with mechanisms, trade-offs, and validation.

WeakNames argmin approximation without operational implications.

limitations

weight 20%

StrongExplains limitations with mechanisms, trade-offs, and validation.

WeakNames limitations without operational implications.

Then expect these follow-ups

Which assumption is most likely to invalidate your approach?
Tests: depth
What would you measure offline and in production?
Tests: evaluation
How would your answer change with one-tenth the data or compute?
Tests: adaptability

Hiring now

OpenAI has 728 open roles right now

landed applies to roles like these for you, on each company’s own site, with answers written for the role.

Get 5x interviews10 free auto-applies. No card.

Integrated Marketing Manager - Consumer & Brand, LATAM
Remote
Partner Director, IBM North America
Remote
Partner Director, EY North America
Remote

Or practice this question first